USE CASES
Outcomes a regulated Indian enterprise can defend to its regulator.
Scenarios from BFSI and BPO/ITES — each stated the same way: the situation, what changes, and why it holds up under audit — plus a defence & public-safety band on sovereign edge AI for air surveillance. Taksha, our coding family, is in developers’ hands today and powers the modernization and testing scenarios below. For the domain specialists named here, Kuber for BFSI and Seva for service operations, talk to us about your workflow, or see the portfolio page.
BFSI
Banking, insurance and capital markets — inside the perimeter.
Sovereign by design: everything below runs inside the institution’s own perimeter, in-VPC — designed so prompts, documents and data stay there. Knowledge bases are built from public and statutory sources only, and every retrieved fact carries its provenance.
Grounded answers over regulatory circulars
Kuber
Situation
Compliance, operations and audit teams work across a sprawling, constantly-amended body of regulation — banking master directions, securities regulations and master circulars, insurance regulations, and the cross-cutting data-protection and anti-money-laundering statutes. Finding the current, correct rule for a given question — and the source that backs it — is slow and error-prone, and a wrong answer carries real liability.
What changes
Kuber indexes this public regulatory body inside your VPC, with provenance on every passage, and answers a plain-language question — what's the periodic KYC-update requirement for a low-risk customer? — with the answer and the exact regulation and passage cited. It is built as a copilot that cites its sources, so a human can verify in one click, not a black box that asks to be trusted.
Why it holds up
The knowledge base is assembled only from public and statutory material, so there is no licensing or PII exposure; retrieval is provenance-tracked, so every answer is traceable to a named regulation; and it runs entirely within the institution's perimeter, aligned with data-residency and data-protection obligations. The retrieval layer — finding and citing the right passage — is the mature piece today; consistently complete answers are the active development focus.
Verifiable modernization of legacy core systems
Taksha
Situation
Much of BFSI still runs on decades-old core systems. Modernization is high-risk and slow, and the genuinely hard part is equivalence — proving the new implementation does exactly what the old one did, especially for regulation-critical logic where a subtle behavioural change is a compliance event.
What changes
Taksha's modernization capability takes legacy modules and produces modern-stack equivalents in-VPC — and, crucially, generates equivalence tests alongside the code, so a migration is verified rather than taken on trust. The legacy source is designed to stay inside the institution.
Why it holds up
The approach is verification-first — generate, compile, run, and check behavioural equivalence — so the output is auditable evidence rather than an assertion, and the whole loop stays inside the perimeter. This capability is being extended to the BFSI vertical's specific stacks and conventions.
Audit-grade test coverage for regulated software
Taksha
Situation
Changes to BFSI software must be provably tested to satisfy internal audit and regulatory expectations. Manual test-writing lags delivery, and coverage gaps become liabilities discovered at the worst time.
What changes
The platform generates audit-grade test suites — acceptance and golden tests tied to real requirements — for BFSI applications, applying the same verify-first discipline used to gate our own models. Changes can then ship with defensible, evidence-backed coverage instead of a manual best-effort.
Why it holds up
Generated tests are executed, not just written, producing runnable evidence of what is and isn't covered — and, like everything else here, this runs in-VPC, so nothing about the institution's systems is exposed.
BPO & ITES
Three delivery factories, one sovereign layer.
A delivery organization’s product is governed throughput: work done at scale, to spec, provably, under someone else’s compliance regime. The blocker to AI in that world has never been capability — it is that the client’s data cannot leave its jurisdiction and every decision must be auditable. A sovereign layer that runs in the client’s region or VPC removes that blocker — the same unlock across all three factories below.
CX floors — agent assist and 100% QA
Seva
Situation
A CX floor runs thousands of agents across regulated accounts. QA samples a few percent of interactions by hand, days or weeks late; the rest go unreviewed. A missed disclosure, a mis-sell, or a PII slip becomes the client's fine and the floor's risk. After-call work eats handle time, and new-agent ramp is slow because quality lives in a few senior heads.
What changes
Every interaction is read the moment it happens and returned as one structured pack: a factual summary, a QA scorecard against a contact-centre standard, a sentiment read, compliance flags (DPDP / PCI) quoted to the exact line that triggered them, a pass/fail verdict with zero-tolerance auto-fail, the agent's best next reply, and a machine-readable workflow action that drops straight into the CRM or ticketing system. The QA analyst's job and the agent-assist job, done in one pass — on 100% of volume instead of a 2–5% sample.
Why it holds up
It runs where the client's data already lives — in-region, in-VPC, on masked inputs — with a recorded audit trail, which is precisely what a bank's or insurer's risk team needs before they let AI touch the account at all. It is built compliance-first: demonstrated on owned, synthetic conversations, never real customer records. And it is scoped honestly — the floor proves it on one queue, against its own baseline, before it scales.
Migration factories — legacy modernization at scale
Taksha
Situation
A modernization factory moves large legacy estates onto modern stacks. The bottleneck is senior-engineer review and inconsistent hand-translation — and, on regulated accounts, a hard rule that source code cannot leave the client's perimeter, which takes hosted coding assistants off the table entirely.
What changes
The sovereign coding model runs the transformation inside the client's perimeter: it produces the modern equivalent of each unit, and only output that passes deterministic checks — compiles, tests green — advances. Verification is the filter, not a claim. The factory's engineers move from hand-translating to reviewing and approving, so throughput becomes consistent and governed instead of dependent on who's on the bench that week.
Why it holds up
The code is designed to stay in the client's VPC — the whole reason their CISO can't approve a hosted copilot — and every transformation is traceable for audit. The intelligence is a model the organization owns and governs, not a rented frontier API that could change or log under it. Quality is gated by verification and a human approves; we don't claim parity we haven't earned.
Test factories — coverage that keeps pace with delivery
Taksha
Situation
A test factory writes and maintains suites across delivery. Coverage is uneven, regression suites lag releases, and — on regulated work — test data can't contain real customer records, which limits what the team can safely fixture.
What changes
The sovereign model drafts tests grounded in the actual code and specs — unit, integration, regression — using synthetic, owned, zero-PII fixtures, and engineers curate rather than author from scratch. Coverage and regression move with the release train instead of trailing it, and the same governed, in-perimeter setup applies.
Why it holds up
It runs in-perimeter, uses owned synthetic data — no real records in a test fixture, a recurring audit finding removed by construction — and its output is inherently checkable: a generated test either compiles and runs or it doesn't. That makes it a verifiable contributor, not a black box.
The path is the same in each: a short, paid pilot on one floor, estate or suite — in the client’s region, on masked or synthetic data, behind their SSO, measured against their own baseline — then scale on the numbers. We deliberately claim no operational numbers before that pilot earns them.
DEFENCE & PUBLIC SAFETY
Air surveillance on the edge — and a person still decides.
The same discipline as every scenario on this page: the AI fuses disagreeing sensors, classifies tracks and ranks threats; a human makes the call. Built to run sovereign and at the edge, so the air picture stays in-country and keeps working when the link is contested — with no engagement authority and no weapon release in the loop.
Air surveillance — a recognised air picture on the edge
Sensor fusion · classification · threat ranking
Situation
A recognised air picture means fusing disagreeing sensors — primary radar, IFF/secondary, an EO/IR turret, a coastal radar — into one trusted view, then deciding which tracks are friendly, hostile or unknown and which matter now. The hardest moment is when the feed is being jammed.
What changes
Edge AI fuses the feeds into one picture, classifies every track friendly, hostile or unknown with its reasons and a confidence, ranks the threats and raises the alert — and keeps detecting and deciding locally when the link is jammed or cut, because the model runs on the sensor site itself.
Why it holds up
The AI supports the decision; a controller makes it. No engagement authority and no weapon release — surveillance and decision-support only — with every recommendation inspectable and the air picture never leaving the site.
Sovereign edge models for air surveillance
Sanjaya · the sovereign edge-AI family
Situation
Defence, coastal and critical-infrastructure surveillance cannot depend on a cloud link an adversary attacks first — and the air picture cannot leave the country. The model has to run inside the perimeter, on hardware you own.
What changes
AgentAnywhere's Sanjaya edge family runs locally with zero external requests, scoring live sensor feeds for integrity, behind a trust layer that signs and records every recommendation so a decision can be reconstructed and defended afterwards.
Why it holds up
Data residency by design, a human in command of every consequential step, and an honest published status for every model family — no fielded-system claims we cannot stand behind.
WHY IT HOLDS UP
Every scenario ships inside the same trust stack — not beside it.
A sovereign model is only as sovereign as the path a request takes to reach it. The scenarios above serve behind the AgentAnywhere trust layer — live today, governing production traffic.
See what this looks like for your institution.
The solution brief states each scenario the way this page does: situation, what changes, why it holds up. Tell us your institution and your constraint, and we will tell you plainly what is ready for it today.