Enterprise AI that reaches production — and stays there.
For banks, hospitals, government departments and large enterprises that need AI to run inside real systems, under real controls. We assess the opportunity, build the agents and automation, secure them, and operate them after go-live.
The pilot worked. Production is a different problem.
Most enterprise AI never fails in the demo. It fails at the point where it has to touch a core system, pass a security review, satisfy a regulator, or run unattended at 2 a.m. That is the part we are built for.
If two or more of these describe your organisation, a 30-minute scoping call will tell you quickly whether we can help.
Sound familiar?
- Three GenAI pilots, zero in production. Nobody owns the path from demo to go-live.
- A copilot answers from public data because nobody could safely connect it to internal documents.
- Security blocked the agent because it could not say what actions it would take, or log them.
- KYC, claims or invoice intake still runs on people re-typing PDFs into the core system.
- The board asks about AI risk and the answer is a vendor’s slide, not your own control.
- Customer or patient data cannot leave your boundary, so every SaaS AI tool is off the table.
- Model cost went up after launch and nobody can say which workflow is driving it.
Find the use cases that are valuable, feasible and safe — in that order.
Enterprises rarely lack AI ideas. They lack a ranked list of ideas that clear the data, integration and compliance bar. Our assessment produces that list, with the first build scoped and costed.
- Workflow-level opportunity mapping across finance, operations, service and compliance
- Data readiness check: where the documents, records and permissions actually live
- Build, buy or configure recommendation per use case, with model and hosting options
- Risk and regulatory scoping up front — DPDP, sector rules, audit expectations
- A prioritised 12-month roadmap with the first production candidate fully scoped
Six stages from idea to operated system.
One disciplined path, with security and compliance built into the middle of it rather than bolted on at the end. A first production use case typically runs 6–12 weeks end to end.
Week 1–2 · Discover
Workflow walkthroughs, data and system inventory, risk scoping. Output: ranked use cases and a scoped first build.
Week 2–3 · Design
Solution architecture: model choice, retrieval design, agent permissions, integration points, hosting boundary, evaluation plan.
Week 3–8 · Build
Engineer the agent, RAG pipeline or automation against your real data, with human review paths and evaluation sets from day one.
Week 8–9 · Secure
Prompt-injection testing, permission review, data-leakage checks and conventional application testing by our AI security team.
Week 9–10 · Deploy
Controlled rollout with access controls, logging, rollback and sign-off documentation for security and compliance owners.
Ongoing · Operate
Managed AI operations: quality, cost, latency and compliance monitoring, model updates and continuous improvement.
Agents that act inside your systems of record — with a permission list, an audit log and a human above them.
An agent that can read a ticket, check a policy, update a record and draft a response is useful. The same agent without scoped permissions and action logging is a security incident waiting to be filed. We build the first kind.
- Task-specific agents for finance operations, customer service, claims, procurement and IT support
- Explicit tool permissions: every action an agent can take is declared, scoped and reviewable
- Full audit logs of inputs, retrieved context, tool calls and outputs, exportable to your SIEM
- Human-in-the-loop checkpoints for payments, record changes, customer-facing sends and other high-impact steps
- Multi-agent orchestration for workflows that span departments and systems
If security has already blocked an agent once, the blocker was almost certainly permissions and logging — not the model. That is a design problem with a known answer.
Read: why agent pilots stall before productionTurn document-heavy intake into structured, checked, auditable workflows.
The highest-return automation in most regulated organisations is not customer-facing. It is the back-office intake that runs on PDFs, scans and email attachments. We extract, validate, route and reconcile — and send exceptions to a person.
KYC & onboarding intake
Identity documents, address proofs and forms extracted, cross-checked against each other and flagged for mismatches before a maker-checker review.
Claims processing
Claim forms, bills and supporting reports classified and structured; policy rules applied; low-confidence or high-value cases routed to an assessor.
Invoice & AP automation
Vendor invoices captured from email and portals, matched to purchase orders and receipts, exceptions queued with the reason attached.
Contract review & intake
Key terms, obligations, renewal dates and non-standard clauses extracted into your contract register, with deviations highlighted for legal.
Every automation ships with confidence thresholds, an exception queue and a review interface. Nothing writes to a system of record without a defined approval path.
AI does not end at deployment. Someone has to run it.
Models drift, prompts get changed, retrieval indexes go stale, costs creep and regulators ask for evidence. Avisha Managed AI puts every model, agent, API and application behind one control plane with named owners and defined SLAs.
Quality & drift monitoring
Evaluation sets re-run on every model, prompt or index change; accuracy and refusal rates tracked per workflow.
Cost & latency control
Token spend and response time attributed by application and team, with routing, caching and model-tier changes to hold budgets.
Compliance evidence
Access logs, change records, incident reports and periodic security re-tests packaged for internal audit and regulators.
Copilots that answer from your knowledge — and cite it.
A general-purpose chatbot knows the internet. Your staff need one that knows your policy manual, product catalogue, SOPs, circulars and case history, respects who is allowed to see what, and shows its sources.
- Retrieval-augmented generation over documents, databases and ticketing systems, with citations on every answer
- Permission-aware retrieval: users only get answers from content they are already entitled to read
- Internal copilots for operations, service desks, compliance teams, clinicians and relationship managers
- Content generation for reports, summaries and correspondence with review before anything leaves the building
- Evaluation harness measuring groundedness and refusal behaviour before and after go-live
When the data cannot leave, bring the model in.
Customer financial records, patient data, citizen records and board material do not belong in a third-party prompt log. We deploy and operate models inside your own cloud tenancy or on-premises, so data residency, DPDP obligations and sector rules are met by design.
- Open-weight and enterprise models hosted in your VPC, data centre or India-region cloud
- No training on your data; no prompts or outputs retained outside your boundary
- Model gateway with authentication, rate limits, content controls and full request logging
- Sizing, GPU and cost planning so private AI is affordable, not just compliant
- Hybrid designs that keep sensitive workloads private while using hosted models for the rest
Connected to the systems that actually run the business.
AI that cannot read from or write to your systems of record is a chat window. We integrate through your existing APIs, middleware and access controls — never around them.
ERP & finance
Purchase-to-pay, reconciliation and reporting workflows connected to SAP, Oracle, Microsoft Dynamics and Tally-class systems.
CRM & service
Case summaries, next-best-action and drafted responses inside Salesforce, Dynamics, Zoho and ticketing platforms.
Core banking & lending
Read-only enrichment and controlled write-backs to core banking, LOS and LMS platforms via approved APIs and maker-checker flows.
HIS, LIS & EMR
Clinical documentation, discharge summaries and coding support integrated with hospital information systems under clinician sign-off.
Pilot culture versus production discipline.
Without a structured programme
- Use cases picked by enthusiasm, not by data readiness or value
- Security and compliance consulted after the build, then block it
- Agents with broad credentials and no record of what they did
- Copilots that hallucinate because they were never grounded or evaluated
- Sensitive data sent to whichever API was easiest to call
- Nobody owns quality, cost or incidents once the vendor leaves
With Avisha Global
- Ranked use cases with feasibility, risk and value scored before any build
- AI security testing and governance built into stages 4 and 5 of every project
- Declared permissions, audit logs and human approval on every agent
- Citations, permission-aware retrieval and evaluation sets on every copilot
- Private or hybrid hosting chosen per workload and data class
- Managed operations with named owners, SLAs and compliance evidence
Concrete deliverables at every stage.
AI opportunity assessment report
Ranked use cases with value, feasibility and risk scoring, data-readiness findings and a 12-month roadmap.
Solution architecture pack
Model and hosting decision, retrieval and integration design, agent permission model, evaluation plan and cost estimate.
Production-grade AI system
The agent, copilot or automation itself — deployed into your environment with source, documentation and runbooks handed over.
AI security test report
Prompt-injection, data-leakage and permission findings with severity, evidence and confirmed fixes before go-live.
Governance & compliance file
Data flows, DPDP mapping, access policy, audit-log design and human-oversight controls, ready for internal audit.
Managed AI operations plan
Monitoring dashboards, SLAs, change process, cost budget and a monthly quality and risk report for owners.
AI, security and compliance from one team — because in a regulated enterprise they are one problem.
AI engineers and AI security testers on the same project
The people who red-team LLMs and test prompt injection review what we build before your CISO does. No hand-off between vendors, no surprises at security sign-off.
Built for the Indian regulatory context
DPDP, sector regulator expectations, data residency and audit evidence are designed in from discovery, not added after a compliance review.
Human-in-the-loop by default
Every agent and automation ships with approval paths, confidence thresholds and an exception queue. Autonomy is expanded deliberately, with evidence.
Production focus, not pilot theatre
We scope the first use case for go-live, not for a demo. Integration, logging, rollback and operations are in the plan from week one.
Product-backed practice
We run our own AI-native products — Avisha Trust and Study24by7 — so the operating problems we solve for you are ones we live with ourselves.
Your data, your boundary, your IP
Private hosting when needed, no training on your data, and full ownership of the systems, prompts and evaluation sets we build for you.
You probably already know which workflow is worth automating — KYC intake, claims, invoices, the service desk. The hard part is proving it clears the data, integration and DPDP bar before anyone funds a build. That is what the assessment is for.
Scope your first production use caseFrequently asked questions
Ready to move past the pilot stage?
Bring us one workflow that matters. We will tell you within a scoping call whether it is worth building, what it will take, and what it should cost.