Illustrative blueprints, not client case studies. Each one describes how we would structure an engagement for a typical organisation at that stage. The organisations are composites, the timelines are typical, and the measures are the targets we would agree with you at the start. They are not results we are claiming.
Blueprint 01 · Banking & financial services
From stalled pilots to a governed production copilot
Stage 02 → 03Mid-size retail bank16–20 weeks to first production release
A servicing copilot has impressed in demos for a year but cannot get past model risk. The work is less about the model and more about making every answer explainable, current and switch-off-able.
Where they start
- Three or four GenAI pilots running in parallel, each with its own stack
- A servicing copilot that works in demos but is not approved for live customers
- Policy documents spread across intranet pages, PDFs and email
What blocks them
- Model risk cannot see why the copilot said what it said
- No evaluation set, so nobody can show it has improved
- Retrieval pulls outdated policy, which compliance treats as a stop
01
Weeks 1–2
Assess & align
Readiness audit across the pilots. Pick the one with the clearest value and a credible path through model risk. Pause the rest in writing.
02
Weeks 3–10
Build & integrate
Rebuild retrieval on governed, versioned policy sources. Add citations to every answer, reason logging and a human hand-off. Create a scored evaluation set with the servicing team.
03
Weeks 11–20
Operate & release
Model risk review with evidence packs. Shadow mode, then a controlled release to one team, with drift, cost and answer-quality dashboards.
04
After release
Transfer & scale
Runbooks and pairing for the bank’s own engineers. A reusable copilot pattern the next two use cases can follow.
Pod we would deploy
- Solution architect
- AI / ML engineers (×2)
- Data engineer
- QA engineer for evaluation and red-teaming
- Delivery lead
What we would agree to measure
- Share of answers with a correct, current citation
- Average handle time on the in-scope query types
- Escalation rate from copilot to a human agent
- Time to produce evidence for a model risk review
- Cost per resolved query
Blueprint 02 · Telecom
Network operations: from alarm storms to AI-assisted triage
Stage 03 → 04Regional mobile and fixed operator12–16 weeks to first production release
An operations centre already has point solutions that correlate alarms, but they are fragile and depend on two people. The goal is an operated, monitored capability that keeps improving instead of decaying.
Where they start
- Rule-based correlation built up over years, owned by a small team
- A proof of concept for ML-based root-cause suggestions that never left the lab
- Field dispatch decisions made from spreadsheets and experience
What blocks them
- Alarm volume spikes break the batch pipeline exactly when it matters
- No feedback loop, so the model never learns from what engineers actually fixed
- No one owns the model in production
01
Weeks 1–2
Assess & align
Map the alarm pipeline, data quality and incident history. Agree which incident classes are in scope and how success will be scored.
02
Weeks 3–8
Build & integrate
Streaming ingestion sized for peak load. Correlation and probable-cause suggestions shown inside the existing NOC console, not a new tool.
03
Weeks 9–16
Operate & optimise
MLOps pipeline with drift and latency monitoring. Engineer feedback captured on every incident and fed back into retraining.
04
Ongoing
Transfer & scale
Managed service with agreed service levels, or hand-over to the operator’s own team, with predictive dispatch as the next use case.
Pod we would deploy
- MLOps engineer
- Data engineer (streaming)
- ML engineer
- Site reliability engineer
- Delivery lead
What we would agree to measure
- Alarms per actionable incident
- Mean time to identify probable cause
- Share of suggestions accepted by NOC engineers
- Pipeline latency at peak alarm volume
- Repeat truck rolls on in-scope faults
Blueprint 03 · Global capability centre
Turning a GCC into an AI Center of Excellence
Stage 01 → 04Global enterprise with an India GCC6–12 months, build-operate-transfer
An established India centre delivers application work well, but AI happens elsewhere in the group. Leadership wants the centre to become the place AI capability is built, run and governed for the whole enterprise.
Where they start
- A cost-led GCC with strong engineering but few AI or MLOps specialists
- Business units buying AI pilots from different vendors
- No shared platform, standards or governance forum
What blocks them
- Hiring AI talent through usual channels takes quarters, not weeks
- No reference architecture, so every team starts from zero
- The centre is seen as a delivery arm, not a source of capability
01
Months 0–1
Assess & align
Readiness and operating-model review with group and GCC leadership. Agree the CoE charter, first three use cases and the transfer conditions.
02
Months 1–4
Build
Embedded Vantidge pod plus accelerated hiring through TalentForge. Shared platform, reference architecture and evaluation standards stood up.
03
Months 4–9
Operate
Run the first use cases to production from the centre. Governance forum in place, reusable components published to other teams.
04
Agreed trigger
Transfer
Roles, runbooks and platform ownership move to the GCC on the conditions agreed at the start.
Pod we would deploy
- AI CoE lead / solution architect
- ML and MLOps engineers
- Data engineers
- QA and evaluation engineer
- TalentForge domain lead for hiring
- Delivery lead
What we would agree to measure
- Time to fill AI and MLOps roles
- Use cases in production run from the centre
- Reuse of shared platform components across teams
- Share of AI work brought in from external vendors
- Readiness against the agreed transfer conditions