For Enterprises · 250+ employees · GCC · India · Africa · APAC

Transformation programs that survive the org chart.

Multi-quarter AI transformation programs for organizations 250+ in size — with executive sponsorship, governance, procurement-ready terms, and senior Microsoft-certified delivery in your region. Six engagement models from Strategic Diagnostic to AI Center of Excellence, on one contract or staged over years.

Request a scoping conversation See client case studies

MSA-ready Senior delivery Stack-flexible Regional fluency Governance-grade

Six Engagement Models — In Detail

Programs that match how enterprises actually buy.

Each engagement is one phase (or a cross-phase capability) of the Gennoor Way, priced and scoped for enterprise complexity. Run them sequentially, in parallel, or combined.

Strategic AI Diagnostic

Phase 1 · Diagnose

4 weeks$25k–$80k

A board-ready AI transformation plan grounded in your organization, not benchmarks from someone else's industry.

The problem we solve

Your board is asking about AI strategy. Your CIO has six vendor decks on their desk. Two business units have already started shadow-IT pilots. Procurement wants a master vendor. Audit wants a governance framework. You need a single, credible, defensible plan that aligns the org — and you need it before next quarter's board meeting.

What we deliver

Executive & operating-team interview synthesis

15–25 structured interviews across C-suite, function heads, IT leadership, audit/risk, and two business-unit operating teams. Output is a confidential synthesis of where the organization stands — not the polite version.

AI Readiness Index — 5 dimensions, scored

Strategy, Data, People, Tech, Governance — each scored 1–5 with evidence and benchmarked against peer organizations in your sector and region (GCC, India, Africa anonymized peer set).

Use Case Backlog — 30+ candidates, scored

Sourced from interviews, your strategy documents, and our reference patterns. Each scored on impact × feasibility × regulatory risk × time-to-value. Output is a quadrant chart your board will recognize.

18-Month AI Roadmap with capital plan

Three waves of work over 18 months. Each wave has scoped use cases, budget envelope, FTE requirement, vendor decision, and success criteria. Designed to flow directly into your annual planning cycle.

AI Governance & Risk Charter

A 12–20 page charter covering: ethical use principles, role-based approvals, vendor governance, data-handling rules, incident response, audit trail. Aligned with NIST AI RMF, EU AI Act, and regional regulations (Saudi PDPL, India DPDP, UAE PDPL).

Executive Briefing Deck + Board Pack

Two artifacts. The briefing deck is for your leadership team. The board pack is a 6–8 page document designed to fit your board's reading rhythm — no animations, no walls of text, just the 7 questions a board needs answered.

On-site executive offsite facilitation (optional)

One-day strategy offsite, facilitated by us, to align your top team behind the roadmap. Off-the-record working sessions with a neutral facilitator who knows the AI domain.

Timeline

Week 1Kickoff · stakeholder mapping · 8–12 interviews · data + tech inventory begins
Week 2Remaining interviews · regulatory posture · current vendor inventory · use case generation
Week 3Use case scoring workshop · roadmap drafting · governance charter draft
Week 4Board pack drafting · executive readout · optional offsite · final delivery

What success looks like

CEO and CIO aligned on a single, written 18-month plan
Board signs off on the AI capital allocation in one cycle
Audit and Risk have a governance charter to assess vendors against
Shadow-IT pilots either folded into the plan or shut down with reason given

Reference engagement

Ministry-level public sector body in the Kingdom of Saudi Arabia — 50+ senior executives. 10-day intensive program blending strategic diagnostic with Vision 2030 alignment. Outputs included AI Governance Charter, organizational AI maturity benchmark across participating bodies, AI Value Maps identifying quick wins and long-term impact, and a network of Saudi AI leaders. (Public-sector reference available under NDA in scoping conversation.)

Not included

Implementation of any use case (E3 onward)
Vendor RFP execution (separate engagement)
Org-design or hiring (different consulting scope)
Data migration or platform consolidation (architecture work, separate)

Pairs well with

E2 Executive & Functional Bootcamps — run in parallel to build literacy while strategy is being set · E3 Strategic Pilot — runs immediately after to demonstrate the roadmap is real

Executive & Functional Bootcamps

Phase 2 · Train

3–10 days · multi-cohort$30k–$150k per program

An AI-literate organization across levels — boards, executives, function leads, technical guilds — speaking the same language about strategy, risk, and ROI.

The problem we solve

Your strategy is set, but the org isn't ready to execute. The board doesn't know what to ask about AI. The CEO is briefing analysts using buzzwords. Function heads are conflating Copilot with AGI. Your technical team is excited but uncoordinated. AI literacy is the bottleneck — and generic e-learning hasn't fixed it.

What we deliver

C-Suite & Board AI Bootcamp (3–5 days)

For CEOs, COOs, CFOs, CHROs, CDOs, and board directors. Strategy, governance, ROI, M&A under AI, talent & org design, board-level reporting. No code. Heavy on scenarios from peer organizations in your sector. Often run as a 5-day offsite.

Functional Cohorts (5–10 days each)

Separate tracks for HR, Finance, Sales/CX, Operations, Legal/Risk, IT/Engineering. Each track is built around the workflows of that function with custom labs using your data scenarios (anonymized). Cohort sizes 15–25 per track.

Technical Guild Track (5–10 days)

For your AI/ML engineering team and citizen developers. Topics: prompt engineering, agent design, RAG architecture, Copilot Studio, evaluation, MLOps. Hands-on labs on your stack of choice — Azure OpenAI, AWS Bedrock, Google Vertex, or open-source.

Custom curriculum with your data

Every cohort runs labs on anonymized scenarios from your actual operations — not generic "imagine a retail company" cases. We co-author the curriculum with one of your operating teams.

Post-bootcamp adoption playbooks per cohort

Each cohort leaves with a 30-90 day adoption plan, role-specific habit tracking, and a "phase 3 pilot scoping" session — every bootcamp ends with at least one named use case ready for E3 Strategic Pilot.

Microsoft & ecosystem partner engagement

Where relevant and welcome, we facilitate panels and demos with Microsoft, Microsoft Arabia, AWS, or industry guest speakers. Especially valuable for board and C-suite cohorts.

Timeline

Weeks 1–2Curriculum customization · stakeholder onboarding · cohort selection · pre-reads
Weeks 3–4C-Suite bootcamp delivery (3–5 days) · board engagement
Weeks 5–8Functional cohorts in parallel · weekly progress with sponsors
Weeks 9–10Technical guild track · pilot scoping sessions · 30/60/90 plan handover

What success looks like

Board members ask sharper questions about AI by week 4
C-suite uses the same vocabulary as the technical team within 8 weeks
Each function has a written 30-90 day adoption plan owned by an internal champion
At least 3 pilots are scoped and ready to enter E3 by end of program

Reference engagement

MCIT, Kingdom of Saudi Arabia — 10-day AI Leadership Mastery program for 50+ senior executives across multiple ministries. Two weeks blending strategy with applied innovation. Executives left with actionable AI roadmaps for their organizations, organizational AI Governance Charters, and AI Value Maps identifying quick wins. Strategies aligned with Saudi Vision 2030 and national AI initiatives. (Named case study available on /case-studies.)

Not included

Building any production system (E3 onward)
Ongoing L&D outside the bootcamp window (covered in E5 Sustain)
Certification exam delivery (we prepare you; certifications are taken with the vendor directly)

Pairs well with

E1 Strategic Diagnostic — bootcamp insights feed the roadmap, and vice versa · E3 Strategic Pilot — pilots scoped during bootcamp cohorts enter E3 directly

Strategic Pilot

Phase 3 · Innovate

6–8 weeks$50k–$180k

A working AI capability deployed inside your enterprise environment, evaluated under your governance, with a written go/no-go business case for scale.

The problem we solve

You've approved the use case. The vendor demos look great. But you've been burned before — pilots that worked on the vendor's laptop but never made it to production because of data, integration, governance, or just plain politics. You want this pilot to be production-grade from day one.

What we deliver

Production-grade architecture from day one

Not a demo. Built on enterprise patterns — RBAC, secrets management, audit logging, observability, cost monitoring. Designed to scale; not designed to throw away.

Stack Fit Assessment (written, defensible)

Cloud LLM (Azure OpenAI, AWS Bedrock, Google Vertex) vs self-hosted open-source (Llama, Mistral, Phi). Cost per 1M tokens at projected scale. Latency targets. Sovereignty. Fine-tuning option. We deliver the assessment; you choose the path.

Multi-team coordination

Pilots typically involve 3–6 internal teams: business sponsor, IT, security, data team, end-users, audit. We run weekly steering committees and provide a single point of accountability across all of them.

Compliance & Risk pre-review

Before week 4, the pilot has been reviewed by your compliance and risk teams against the governance charter (delivered in E1 or shared at kickoff). Findings logged. Issues resolved before user testing.

Working system + evaluation harness

A working pilot in your environment that 25–100 users can interact with. Plus a continuously-running evaluation harness measuring accuracy, latency, cost, and user satisfaction.

Go/no-go business case for Phase 4

A 10–15 page business case at the end of the pilot. Quantified ROI, scaling assumptions, risk register, FTE and infra requirements at full scale. Designed for your investment committee, not for a sales pitch.

Code in your repository · IP yours

Every commit is in your repository from day one. We do not host. Your team has read access throughout, write access from week 4.

Timeline

Week 1Kickoff · stakeholder alignment · Stack Fit · scope + acceptance criteria · governance pre-check
Week 2Architecture sign-off · first slice (mockup → demo Friday) · data access workflow
Week 3Core capability build · integration with 1–2 enterprise systems · evaluation harness up
Week 4Compliance review · risk register · expanded user-feedback group
Week 5Hardening · UAT · cost optimization sprint · stakeholder demo
Week 6Final tuning · business case drafting · go/no-go readout · phase gate
Weeks 7–8(Optional extension for complex integrations or multi-team UAT)

What success looks like

A working pilot used by real internal users — not a vendor demo
Compliance and Risk sign off without "but" attached
Accuracy and cost metrics inside the targets defined at week 1
A go/no-go business case approved by your investment committee
Internal team can deploy a code change without us by end of pilot

Reference engagement

Tier-1 retail bank in India — multimodal RAG system for financial documents (banking transactions including SWIFT, IBAN, dates that pure vector search misses, plus charts that needed visual interpretation). 8-week pilot on Azure AI Search + Azure AI Vision + GPT-4o. Outcomes: 94.2% text accuracy, 91.8% chart understanding, 100% citation coverage, 2.1s average query speed. Business case approved for Phase 4 scale-out to 5 additional document categories.

Not included

Production scale-out to >100 users in this engagement (E4)
Integration with more than 3 enterprise systems (extends scope)
New use cases beyond the one scoped at kickoff (extends scope or new E3)
Custom procurement processes for vendor onboarding (separate vendor mgmt scope)

Pairs well with

E4 Transformation Program — the natural next step if pilot business case approves · E2 Functional Bootcamp — runs in parallel to prepare the receiving team

Transformation Program

Phase 4 · Build

9–20 weeks$150k–$600k+

A scaled, governed, production-grade AI capability owned and operated by your internal team — with a transition plan that is contractually obligated, not aspirational.

The problem we solve

You have a pilot that works. Now you need it deployed at enterprise scale — across thousands of users, integrated into 5+ enterprise systems, monitored by SRE, governed by audit, and ultimately owned and operated by your internal team. And you cannot afford the typical SI pattern where the partner builds it and never leaves.

What we deliver

Production deployment on your subscription

Multi-environment (dev / staging / prod) on your Azure / AWS / GCP subscription. Infrastructure-as-code (Terraform / Bicep / CloudFormation). Network and identity in your topology, governed by your CISO.

CI/CD, MLOps, observability

Automated build/test/deploy pipelines. Model evaluation harness running on every release. Application Insights / DataDog / your observability stack integrated. Alerting tied to your existing on-call.

Co-build with your engineering team

Knowledge transfer is contractual, not optional. From week 1, your engineers shadow and pair on every commit. By Go-Live, your team has merged code, run a deployment, and resolved an incident — without us in the room.

Adoption & change management

A dedicated change-management workstream: communications plan, training cohorts, internal champion programs, adoption dashboards, executive reporting. Especially critical for org-wide rollouts.

Multi-team coordination & steering

Weekly steering committee. Fortnightly executive update. Quarterly board update if scope warrants. Single point of accountability across IT, security, data, business sponsor, and your operating teams.

Operations runbook + on-call handover

Written operations runbook owned by your team. Sample incidents resolved jointly. Your on-call rota updated to include this system. We extend hypercare for 4 weeks post Go-Live.

Adoption Metrics Dashboard

Power BI or your analytics stack. Daily active users, success rate by use case, cost per interaction, satisfaction scores. The system the business sponsor uses to report ROI quarterly.

Timeline

Weeks 1–2Detailed solution architecture · environment provisioning · security & identity setup · change-management kickoff
Weeks 3–6Core build · integration with enterprise systems · client-team co-build · weekly demos
Weeks 7–10Hardening · scale testing · UAT cohorts · governance compliance verification
Weeks 11–14Phased rollout (departments / regions / business units) · adoption tracking · iteration
Weeks 15–18Full deployment · knowledge transfer verification · runbook finalization
Weeks 19–20Go-Live · 4-week hypercare begins · sustained-state transition to client team

What success looks like

System operating in production with target SLAs met for 30+ days
Internal team has resolved at least 3 production incidents without us
Adoption metrics dashboard showing sustained usage curve, not a launch-spike
Cost per interaction inside the budget envelope set at E3
Business sponsor reporting quarterly ROI to leadership using the dashboard

Reference engagement

East African central bank — 10-day AI Agents Implementation program for digital innovation team, followed by a multi-quarter co-build for AI agents in banking automation (multi-agent systems with blockchain integration for clearing). Knowledge transfer was contractual; internal team took full ownership of operations within 4 months. (Anonymized for confidentiality; named reference under NDA.)

Not included

Ongoing operations beyond hypercare (covered by E5 Sustain)
Brand-new use cases beyond the scaled pilot (new E3 → E4 cycle)
Custom hardware or air-gapped infrastructure procurement (we deliver on what you provide)
Custom enterprise app development outside the AI surface (separate engineering scope)

Pairs well with

E5 Enterprise Sustain — strongly recommended, contracted at Go-Live · E6 AI CoE — natural next step when the org wants to standardize AI capability internally

Enterprise Sustain

Phase 5 · Sustain

Annual contract · quarterly cadence$15k–$60k/month

Every system you deployed still performing — technically, economically, organizationally — through model evolution, regulatory shifts, and organizational change.

The problem we solve

You've scaled three AI systems to production. Models are drifting. Token spend has tripled. Two regulations changed last quarter. Your AI champion just left for a competitor. And the next board meeting is asking what happens if your vendor disappears. You need a senior partner in the room — not 24/7 managed services, but the right expertise at the right cadence.

What we deliver

Quarterly Health Check per system (1–3 days each)

Model evaluation on a refreshed test set. Drift analysis. Cost & token audit. Security & governance refresh. Output: written Health Report with prioritized action list and executive summary.

Monthly steering review

Senior practitioner in your steering room every month. AI portfolio status, risk review, vendor performance review, upcoming use case pipeline. Designed to fit your existing governance rhythm.

Continuous L&D refresh

Quarterly micro-learning drops for each cohort trained in E2. New techniques (e.g., new model releases, new agent patterns). Plus refresher workshops for new joiners in critical functions.

Annual Strategy Day

One full day per year — in person or virtual — planning the next 12 months of AI work. AI portfolio expansion, retirement decisions, budget alignment, capability gap analysis.

New Use Case Incubation Pipeline

Continuous pipeline of new use cases identified, scored, and triaged. Use cases that pass triage move into new E3 Strategic Pilot engagements (priced separately from the retainer).

Regulatory & Compliance Refresh

Quarterly review of regulatory changes (EU AI Act, NIST AI RMF, regional rules — Saudi PDPL, India DPDP, UAE PDPL). Update of governance charter as needed. Annual audit-readiness pack.

Vendor & Cost Optimization Review

Quarterly audit of LLM vendor mix, model selection, caching strategy, prompt compression, batch opportunities. Typical savings cover 30–60% of the retainer cost in year one.

Incident response (4-hour SLA for sev-1)

Senior practitioner reachable within 4 hours for severity-1 incidents. Not a 24/7 MSP — a senior expert within hours, every time.

Timeline

Month 1Baseline assessment across all deployed systems · stakeholder mapping · retainer rhythm setup
MonthlySteering review · async support · ad-hoc consultation
QuarterlyFull health check per system · written report · regulatory refresh · cost audit
AnnuallyStrategy day · capability gap analysis · next-year budget planning support

What success looks like

Performance metrics across all systems flat or improving year over year
Total AI cost trending down as a % of value delivered
Zero regulatory non-compliance findings in audits
New use case pipeline producing 2–4 new E3 Pilots per year
Internal AI champion turnover does not cause capability loss

Reference engagement

Multi-business-unit conglomerate in GCC — $42k/month Enterprise Sustain across 4 deployed AI systems. In year one: caught a $180k/year token-spend optimization opportunity (model right-sizing + caching), led the response to a regional regulatory update, retrained two functional cohorts after their original champions moved internally.

Not included

Building new use cases (handled through new E3 engagements)
24/7 on-call / managed services (not an MSP)
Long-form custom development sprints (handled through E3 / E4)

Pairs well with

Contracted at every E4 Go-Live · Often combined with E6 AI CoE as the operating engine

AI Center of Excellence (CoE) Setup

Cross-Phase · Capability Building

12–20 weeks$80k–$250k

A functioning internal AI Center of Excellence your organization owns and operates — charter, operating model, tooling, governance, and the first 90 days of operating cadence.

The problem we solve

You've done 2–3 AI projects with external partners. Each was good. Each was disconnected. There's no shared platform, no shared governance, no shared talent pool, no shared learning. The next 10 projects can't look like the last 3. You need an internal capability that compounds — and you need it stood up correctly, once.

What we deliver

CoE Charter & Operating Model

Written charter: mission, scope, decision rights, funding model, success metrics. Operating model: hub-and-spoke vs federated vs hybrid, with explicit interfaces to business units, IT, audit, and procurement.

Role Design & Hiring Profiles

6–12 role definitions: CoE lead, solutions architect, ML engineer, MLOps engineer, governance lead, adoption lead, AI program manager. Each with hiring profile, market salary band (region-adjusted), interview rubric.

Tooling & Platform Setup

Shared platform: LLM gateway, vector store, evaluation harness, prompt management, observability, governance dashboard. Built on your stack (Azure ML / SageMaker / Vertex / open-source).

Governance Framework Operationalized

The governance charter from E1 made operational: intake workflow, risk classification, ethics review board structure, vendor management, model registry. All wired to your existing GRC processes.

Use Case Intake & Prioritization Engine

A repeatable process for collecting use cases from business units, scoring them, triaging through the CoE, and feeding qualified candidates into pilot engagements.

Internal Enablement Engine

Reusable internal training (built from our courses), prompt library, pattern library, internal blog, monthly community of practice meetings. The components that turn one-off training into sustained capability.

90-Day Operating Cadence with us embedded

For 90 days post-launch, we sit in the CoE as senior practitioners. Pair with your CoE lead. Co-run the first intake cycles. Coach through the first governance escalations. Then we transition out.

Timeline

Weeks 1–3Charter co-authoring · operating model design · stakeholder alignment · funding model
Weeks 4–8Tooling & platform build · governance operationalization · role hiring kicked off
Weeks 9–12CoE soft launch · first intake cycles · pattern library populated
Weeks 13–20Full operating cadence · we coach in-room · transition to client ownership · 90-day review

What success looks like

CoE is processing 5–15 use case intakes per quarter by Go-Live
Average time from idea to scoped pilot is under 6 weeks
Internal pattern library and prompt library are net-additive each quarter
CoE lead is reporting to executive committee with confidence
External AI vendor spend is trending down as internal capability grows

Reference engagement

Multi-bank financial services group, GCC — 16-week AI CoE setup covering charter, platform on Azure ML, governance integration with existing GRC, and 90-day in-room coaching. By month 6 post-launch the CoE was running an active use case backlog of 24 candidates across the group, had standardized vendor SLAs across 3 AI vendors, and had reduced external pilot scoping time from ~8 weeks to ~4 weeks.

Not included

Hiring of the CoE team (we design the roles; you hire)
Direct delivery of use cases through the CoE (those are E3/E4)
Ongoing operating support beyond the 90-day embed (covered in E5)

Pairs well with

Most effective after at least one E3+E4 cycle has demonstrated AI value · E5 Sustain typically wraps around the CoE for the first 12–18 months

The Three Pledges

Three commitments that close enterprise procurement faster.

Why CIOs, CDOs, and procurement teams move from RFP to contract with us in weeks, not quarters.

Stack-flexible

We’re Microsoft-strong. We’re not Microsoft-locked. Open-source Llama on your own GPU, Azure OpenAI, AWS Bedrock, Google Vertex — we recommend based on your cost, sovereignty, and compliance needs, not our partnership margins.

Economic

Fixed-price by default for Diagnose, Train, and Innovate. Transparent cost breakdowns in every proposal. Optimization sprints inside every Build engagement. We price what we deliver — not what we estimate.

Consistent

Same five-phase journey for a 30-person SMB in Nairobi and a 30,000-person bank in Riyadh. Same deliverable shape. Same senior delivery on every engagement — no juniors substituted in.

Procurement-Ready

The materials your procurement team will ask for.

We design every engagement to clear procurement, legal, security, and audit review without surprises. Here's what we bring to that conversation by default.

NDA & MSA templates

Mutual NDA template available before any data conversation. Standard MSA + per-engagement SOW model. We work with your legal templates too — exchange takes 5–10 working days typically.

Data residency & sovereignty

Data stays in your environment — your subscription, your tenant, your region. We do not store client data. Compliance with regional rules: Saudi PDPL, India DPDP, UAE PDPL, GDPR awareness. Air-gapped / on-prem patterns available.

Regional presence & invoicing

Gennoor Tech Private Limited (India) for INR invoicing and GST-compliant billing. International invoicing supported. On-the-ground delivery experience: India, Saudi Arabia, UAE, Tanzania, Kenya, Singapore.

Vendor & sub-processor list

Transparent list of sub-processors (Azure, GitHub, etc.) provided on request. Security questionnaires (CAIQ-style, ISO27001-aligned controls) answered within 5–10 working days.

References & introductions

Named client references available after initial scoping conversation. Reference calls offered for engagements over $100k. Reference clients across BFSI, public sector, energy, and manufacturing in GCC, India, and East Africa.

IP ownership & code custody

All code, models, fine-tuned weights, and prompts are client-owned. Code in your repository from day one. Our reusable frameworks (templates, evaluation harnesses, methodology) remain ours and are listed in the SOW as reusable IP.

More detail — Trust & Security overview →

At a Glance

All six engagement models, side by side.

Engagement	Phase	Duration	Investment band	Best when
E1 · Strategic AI Diagnostic	P1: Diagnose	4 weeks	$25k–$80k	A board-ready AI transformation plan grounded in your organization, not benchmarks from someone else's industry.
E2 · Executive & Functional Bootcamps	P2: Train	3–10 days · multi-cohort	$30k–$150k per program	An AI-literate organization across levels — boards, executives, function leads, technical guilds — speaking the same language about strategy, risk, and ROI.
E3 · Strategic Pilot	P3: Innovate	6–8 weeks	$50k–$180k	A working AI capability deployed inside your enterprise environment, evaluated under your governance, with a written go/no-go business case for scale.
E4 · Transformation Program	P4: Build	9–20 weeks	$150k–$600k+	A scaled, governed, production-grade AI capability owned and operated by your internal team — with a transition plan that is contractually obligated, not aspirational.
E5 · Enterprise Sustain	P5: Sustain	Annual contract · quarterly cadence	$15k–$60k/month	Every system you deployed still performing — technically, economically, organizationally — through model evolution, regulatory shifts, and organizational change.
E6 · AI Center of Excellence (CoE) Setup	Cross-P· Capability Building	12–20 weeks	$80k–$250k	A functioning internal AI Center of Excellence your organization owns and operates — charter, operating model, tooling, governance, and the first 90 days of operating cadence.

Procurement & Program Questions

The questions enterprise buyers ask.

Procurement, governance, regulatory, and program-level questions — answered with the directness your legal and risk teams expect.

How do you compare to a Big-4 transformation engagement?

Three structural differences. (1) Senior delivery — the practitioner you scope with is the practitioner who delivers; there is no analyst tier between the work and the senior expert. (2) End-to-end on one engagement — diagnostic, training, pilot, build, and sustain on one contract; Big-4 firms typically split this across separate scopes with different teams. (3) Transparent price bands — we publish ranges on the website and lock fixed price in writing before kickoff for Diagnose/Train/Innovate. Most clients see total program cost 40–60% lower than Big-4 quotes for equivalent scope, with comparable or stronger outcomes.

Can you work alongside our existing SI partner (Accenture, Wipro, TCS, Infosys)?

Yes — this is a common arrangement. We act as the AI specialist arm of a broader transformation led by your SI. We co-design with their architects, hand off operations to their managed-services team if relevant, and respect the existing engagement boundaries. We have run several such joint engagements in BFSI and public sector.

Do you accept our enterprise MSA and procurement process?

Yes. We work with client-provided MSA templates regularly. Standard cycle is 2–4 weeks of legal exchange, which we begin in parallel with scoping. We carry standard enterprise terms — IP assignment, IP indemnification, data protection schedule, security schedule, regional compliance attestations. Our preferred SOW format is fixed-price per phase with clear acceptance criteria.

Can you work air-gapped or in our private cloud / on-prem environment?

Yes. Open-source LLMs (Llama, Mistral, Phi, Qwen) on private infrastructure — via Ollama, vLLM, or Azure ML private endpoints — is one of our reference patterns. Common for government, defense, healthcare, and regulated finance workloads. We have delivered air-gapped pilots and full production builds in such environments.

Who actually delivers — and do you have the bench depth for a multi-quarter program?

Engagements are led by senior practitioners (Microsoft Certified Trainers, 14+ years experience). For larger programs (E4 Transformation Program, E6 CoE), the lead is paired with a small team of mid-senior practitioners. We cap active engagements per quarter to maintain quality — for E4-scale engagements with >$300k value, we are transparent about start-date availability. Our model is senior-only delivery, capacity-limited; not a high-velocity staff augmentation shop.

How do you handle regulated industries — banking, healthcare, government?

We have delivered into all three. Approach: (1) regulatory posture is part of the E1 Diagnostic — we map your obligations before scoping any pilot. (2) Compliance review is built into every E3 Pilot at week 4 — explicit gate, not a checkbox. (3) For healthcare and government, we default to private-deployment patterns and PHI/PII isolation. (4) For BFSI, we align with regional regulators (RBI for India, SAMA for Saudi, ADGM for UAE) and integrate with your existing model risk management process where applicable.

What is your model risk management (MRM) approach for regulated clients?

We integrate with your existing MRM framework rather than imposing our own. Practically: every production AI model produced under our engagement is documented in your model registry, tested under your validation regime, and re-validated on the cadence your MRM dictates. Our Sustain phase (E5) includes quarterly evaluation against fresh test sets and an updated model documentation pack ready for MRM submission.

How do you price multi-region or multi-business-unit programs?

For programs spanning multiple regions or BUs, we price per business unit / per region with shared platform components priced once (CoE setup, governance, central platform). This avoids the "one big number" approach where it's impossible to attribute value. Each BU sees a clear engagement scope with its own success criteria and timeline.

What if our internal team can't take over the system post-Build?

Knowledge transfer is contractual on every E4 engagement, and the design assumes your team takes over at Go-Live. If your team is not ready, we extend hypercare (built into the contract). If for any reason your team remains unable to operate, we roll into an E5 Sustain retainer with extended operational coverage (uncommon but possible). The framework is designed so this never becomes a surprise — week-4 readiness checkpoints flag risks early.

How do you handle vendor lock-in concerns — are we married to your stack?

No. Three protections built into every engagement: (1) Code lives in your repositories from day one; you can fork and continue without us. (2) Stack choices are written and justified in the Stack Fit Assessment — you can move from Azure to AWS or to open-source later with documented migration paths. (3) Our reusable frameworks (evaluation harnesses, methodology, course content) are non-confidential and re-implementable; we don't hide value in proprietary lock-in.

Do you provide audit-ready documentation for governance review?

Yes. Every engagement produces a documentation pack designed for audit and risk review: architecture documentation, decision log, vendor selection rationale, evaluation reports, risk register, governance charter compliance map, training records, and post-deployment metrics. Format compatible with your audit team's expectations (we have worked with Big-4 auditors, internal audit teams, and regulators directly).

How do board-level conversations and reporting work?

For engagements that warrant it (E4 Transformation Programs, E6 CoE setups, multi-program retainers), we provide board-ready reporting on a quarterly cadence. The format is short (6–8 pages), executive in tone, and matches your board's existing reading rhythm. We can also brief your board directly at request — typically once per program at a major milestone.

What does success look like at the program level — how do we know it worked?

Three lenses we report on: (1) Operational — systems running, SLAs met, adoption sustained, costs inside envelope. (2) Capability — internal team can operate, hire, and govern; CoE intake pipeline is healthy. (3) Strategic — measurable business impact (revenue, cost, risk, NPS) tied to the original use case ROI hypothesis. We report against these from the first quarterly review onward; nothing is left to interpretation at year-end.

Ready for a scoping conversation?

Most enterprise engagements begin with a 60-minute exploratory call with your CIO/CDO sponsor — no deck, no pitch. We listen, we ask the awkward questions, and within five working days we send a written one-pager on what we'd propose. That's when the procurement conversation starts.

Request a scoping call See client case studies