Dakshanta — AI Delivery Governance Consulting
Your team bought the AI tools.
Now make them ship.
Dakshanta helps engineering organizations turn AI coding assistants from a per-developer experiment into a governed, repeatable delivery system — with agent-based review pipelines, enforced lifecycle gates, and an audit trail your compliance team can live with.
Built by an operator, not a theorist: 15+ years in AI and data products, the last seven running product and engineering simultaneously at two AI companies. That experience became a multi-agent AI governance framework — designed, shipped, and adopted as the standard development process for a fintech lending platform's engineering team, improved weekly through production use.
The problem
AI coding assistants don't fail loudly. They fail plausibly.
Most teams that struggle with AI adoption don't have a tooling problem — they have a process vacuum. The assistant is capable; nothing governs how its output becomes production code.
Plausible, not verified
AI assistants state guesses with total confidence. Designs get written from assumptions about the codebase instead of the codebase itself — and the errors surface weeks later, in review or in production.
Every developer, a different process
One engineer uses AI for tests, another for whole features, a third not at all. Output quality becomes a function of individual habits, and nothing learned by one person transfers to the team.
Review becomes the bottleneck
AI multiplies the volume of code, not the number of senior reviewers. Unreviewed AI output lands in PRs, and your best engineers spend their days catching machine-generated mistakes by hand.
No audit trail
When something ships broken, nobody can reconstruct what the AI decided, what a human approved, or which checks actually ran. In regulated domains, that is not a nice-to-have.
The approach
Governance that runs in the pipeline, not in a slide deck
The framework I bring is not theory — it's the productized version of a system running daily inside a real fintech engineering team, refined through every failure it caught (and every one it initially missed). Five pillars:
01 — Lifecycle gates
Stage gates between requirements, design, code, and PR
Work moves through explicit stages — requirements → technical design → implementation → pull request → release — and each transition is gated: a design cannot start without analyzed requirements, code cannot start without an approved design grounded in a real codebase analysis, and a PR cannot open until review agents and tests have passed.
02 — Adversarial agents
AI that checks the AI
A builder agent writes; independent critic, reviewer, and QA agents evaluate. The critic scores each artifact in isolation, the reviewer checks that requirements, design, and code actually agree with each other, and the QA agent derives test cases from requirements — not from the implementation it would be grading.
03 — Mechanical enforcement
Process the tooling enforces, not prose that asks nicely
Guidelines that live in a wiki get skipped under deadline pressure. The framework wires gates into the development tooling itself — pre-action hooks that block code edits until prerequisites exist, guards that block PR creation until checks are green, and CI jobs that re-verify server-side.
04 — Verification-first culture
No claim without a citation
The single highest-leverage rule: any factual claim an AI makes about your code, data, or architecture must be backed by a tool call that verified it — file and line, query and count. It converts the assistant from a confident guesser into an evidence-driven collaborator.
05 — Continuous evolution
Every failure becomes a rule
When something slips through — a missed edge case, a wasted review cycle — the incident is traced, and the lesson is encoded back into the governance system as a new gate, check, or prompt. The process compounds instead of decaying.
Services
Ways to work together
Engagements are scoped to your team's maturity — from a two-week diagnostic to standing ownership of your AI delivery process.
AI Delivery Assessment
1–2 weeksA structured audit of how your team uses AI today: where it helps, where it silently creates rework, and where the risk concentrates. You get a findings report and a prioritized roadmap — whether or not we work together afterward.
Governance Framework Pilot
4–8 weeksWe design and implement a governance system tailored to your stack and review culture, then run it on a real feature with a real team: lifecycle gates, critic/reviewer/QA agent pipeline, enforcement hooks, and CI integration.
Team Enablement
ongoing or workshopHands-on training for your developers: verification-first prompting, working with agent pipelines, writing AI-consumable requirements and designs, and reviewing AI-generated code without drowning.
Fractional AI Delivery Advisor
retainerContinued ownership of your AI delivery process as it evolves: tuning gates, evaluating new models and tools, encoding lessons from incidents, and keeping governance ahead of adoption.
How it works
From first call to rollout
Intro call
30 minutes. You describe where your team is; I tell you honestly whether I can help.
Assessment
I observe how your team actually builds with AI and map the gaps against the framework.
Pilot
We stand up governance on one team and one real feature — measurable, low blast radius.
Rollout
What works gets hardened, documented, and expanded across teams, with your engineers owning it.
About
Abheek Sambyal — founder, Dakshanta
Dakshanta — from the Sanskrit dakṣa: skilled, able, expert.
I've spent 15+ years building AI and data products — the first decade helping Fortune 500 companies turn data and analytics into decisions, and the last seven running product and engineering simultaneously at two AI companies: as EVP of Product, AI & ML Engineering at Sentrana, an enterprise AI/ML platform company, and as Chief Product Officer & VP of Engineering at GreenLyne, an AI-powered loan underwriting and marketing platform serving a growing network of lenders and channel partners.
That vantage point — owning the roadmap and the delivery org at the same time — is what shaped my answer when AI coding assistants arrived. Adoption doesn't fail on model quality; it fails on the same things software teams have always failed on: unclear requirements, unverified assumptions, inconsistent process, and review that can't keep up. A decade of managing those failure modes in humans is exactly the preparation for managing them in AI.
So I encoded that management into the pipeline: a multi-agent governance framework — lifecycle gates, adversarial critic and reviewer agents, QA agents that derive tests from requirements, and enforcement hooks wired into the development tooling itself. It was adopted as the standard process for a fintech platform's development team, operating in a regulated, compliance-sensitive domain, and it evolves weekly as developers use it on real tickets.
That experience — what worked, what failed, and which rules only got written after something slipped through — is what Dakshanta brings to organizations facing the same adoption curve.
Contact
Tell me where your team is stuck
A short note is enough — what you're building, how your team uses AI today, and what's not working. I read every message and reply personally.