Services / Academy
The forward-deployed engineering discipline doesn't exist as a hiring pipeline anywhere else. So we built one.
Ten weeks, cohort-based, hybrid. Shared foundations for every trainee, then a split into one of two tracks — because verifying one decision and getting anything live at all are different jobs, staffed differently.
10 wks
4 weeks foundations, 2–4 weeks track electives, 2–3 week capstone.
15–25
Trainees per cohort — small enough for every capstone to get a real review.
2 tracks
Sponsored (bonded, tuition-free) or self-funded, open enrollment.
Foundations — required
Four modules. The same discipline whether the job is verification or adoption.
01 · Causal Reasoning
DAGs, confounders, Simpson's paradox, structural causal models — why pattern-matching in language isn't the same as establishing causality in operational data.
1 wk
02 · Verification Architecture
Designing a WHY-traversal tree, writing hard gates (temporal ordering, magnitude consistency, sample-size floors), the three-tier escalation model.
1 wk
03 · Calibration & Confidence
What calibration actually means, Brier scores and reliability diagrams, translating a statistical interval into a label a non-technical approver can act on.
1 wk
04 · Audit & Governance
Immutable decision-log schemas, named-approver workflows, what a model risk committee or regulator actually asks to see.
1 wk
Choose a track after week 4
Two tracks. Two different engagement types on the other side.
Track A — Decision Verification
Staffs verification engagements
Deepens the foundations: production-grade traversal trees, structural causal models for a named vertical, and the audit schema a real risk committee will accept. Ends with a vertical elective.
Track B — AI Adoption & Modernisation
Staffs adoption engagements
Legacy-system reverse engineering — reading undocumented code and inferring the business rules inside it — agentic workflow architecture, and the change-management reality of getting anything new adopted inside a risk-averse org.
Track B exists because most of what blocks enterprise AI adoption isn't a decision-verification problem. Every Track B placement doubles as market research: the hard problems its FDEs hit are the pipeline for new Vertical Verification Packs.
Track A electives
Same verification discipline, different failure patterns.
Financial Services
Available now
NBFC and bank origination pipelines, credit decisioning, collections. Model risk committee expectations, RBI and SOX context.
2 weeks
Commerce & Growth
Available now · with Niti
Marketing spend allocation, attribution reconciliation, margin-aware decisioning — taught jointly with Niti's team, using its own template library as case material.
2 weeks
Claims & Insurance
Roadmap
Prior-auth exceptions, claims triage, fraud-adjacent decisioning under regulatory review.
2 weeks
Trading & Execution
Roadmap
Trade sign-off, position-limit exceptions, execution-quality verification.
2 weeks
Track B electives
Same FDE discipline, aimed at whatever's actually blocking adoption.
Legacy Modernisation
Available now
Reverse-engineering undocumented systems — mainframe, monolith, decades of institutional logic — into a spec an agent can safely act on.
2 weeks
Agentic Workflow Architecture
Available now
Designing and shipping bespoke agent-driven workflows outside the verification frame — the general build-it-and-run-it job.
2 weeks
Regulated Change Management
Roadmap
Getting a genuinely new system adopted inside a compliance-heavy org — the non-technical half of why AI pilots stall.
2 weeks
Capstone
A real, anonymised client problem. Defended to a panel that includes a working engineer.
The brief.
Track A trainees write a constraint framework, causal model spec, calibration approach and audit schema for a real decision problem. Track B trainees get a real adoption blocker and write the modernisation plan.
The review.
Defended live, the same way an engagement gets scoped internally — not a slide deck, a working spec.
The bar.
Top performers are fast-tracked directly into a live engagement in week 11.
Sponsored
12–18 month bond staffing verification or adoption engagements
₹0 tuition
Self-funded
None — certificate, capstone portfolio, alumni job board
₹1.5L one-time
Sponsored seats are limited per cohort and prioritised for partner-college applicants. Self-funded seats are open to anyone who clears the technical screen. First two cohorts run in partnership with two Indian engineering colleges — named once the first cohort is confirmed.
Apply
Apply as a trainee, or tell us which track you'd sponsor.
Say which track you're interested in — Decision Verification or AI Adoption — and whether you're applying sponsored or self-funded.