Agentic AI engineering is the work of turning a language model into a system that completes a business process end to end, with permissions, audit trails, exception handling and a human approval path. We build these on Claude, GPT and Gemini, orchestrated in n8n, inside the operational systems our clients already run.
The pilot to production gap
A demo needs one good example. Production needs every case, including the malformed ones, at 2 am, with an audit trail.
Untrusted data
Duplicate records, undefined metrics, stale references. The model isn't wrong; the record is.
No exception path
The agent hits an unusual case and either guesses or stops. Both are unacceptable in regulated processes.
No audit trail
Someone asks "why did it approve that?" six months later and there's no answer.
Undefined accountability
Nobody agreed who owns the agent's output. SEBI holds the regulated entity responsible for third party AI tools.
No operating model
The agent goes live and then drifts, because nobody funded the person who monitors it.
How we build
Process & data assessment
We pick one process and trace real cases through it. We profile the data and tell you honestly whether it's good enough.
Working agent in your environment
An agent running on your records, handling a defined slice of real volume, with logging switched on.
Controls & exceptions
Confidence thresholds tuned to your risk appetite. Escalation paths with source documents. Permission boundaries and immutable decision logs.
Production & scale
Rollout, user training, monitoring dashboards, evaluation suite against a labelled set of your cases. Then managed operations.
What we build most often
Document agents
Reading incoming files, KYC packs, loan documents, invoices, claim forms, extracting fields, cross checking against records, flagging mismatches.
Voice agents
Outbound qualification, appointment confirmation, payment reminders, feedback collection. Multilingual with mid call switching.
Learn moreKnowledge agents
Cited answers across CRM, Desk, Books, ERP and document stores. Every response points to its source record.
Learn moreWorkflow agents
Multi step processes that touch several systems, reconciliation, exception clearing, status chasing, report assembly.
Drafting agents
First response drafting in support, proposal and email drafting in sales, always reviewed before sending.
Learn moreGovernance we build in by default
Human in the loop
On every decision with financial or customer consequence, with the threshold set by you.
Immutable audit logs
Timestamped, with the source document and the model version that produced the decision.
Permission inheritance
An agent sees only what its requesting user is entitled to see.
Evaluation suites
A labelled set of your real cases, re run on every prompt or model change.
Prompt and version control
Nothing changes in production without a diff and a test.
Build versus subscribe
| Buy a product | Build with us | |
|---|---|---|
| Time to value | Days | 4 weeks to working, a quarter to production |
| Fit to your process | Generic | Exact |
| Data leaves your environment | Often | Only if you choose |
| IP and lock in | Vendor's | Yours |
| Cost shape | Per seat, forever | Project plus run |
| Best for | Standard, low risk tasks | Regulated, complex or differentiating |
Common questions
How long until an agent is in production?
Four weeks to a working agent on your records, roughly a quarter to full production with controls and monitoring.
Which model should we use, Claude, GPT or Gemini?
It depends on the task, and most systems use more than one. We benchmark on your cases during the build.
Where does our data go when the agent runs?
Wherever you require. We deploy inside your cloud tenant or VPC, and for BFSI clients we keep processing in Indian regions.
How do we audit what an agent decided?
Every decision writes an immutable log entry with a timestamp, the source records used, the model version and the confidence score.
What happens when the agent gets it wrong?
It routes to a human with the source document open. For errors above the threshold, the evaluation suite catches drift.
Can agents work inside our existing Zoho, SAP or core banking setup?
Yes, that's the normal case. Agents read and write through APIs and MCP connections with permissions inherited from the requesting user.
Bring us one process
Ninety minutes with our engineers. You’ll leave with an architecture view and an honest answer about whether AI belongs in it yet.
Book a working session