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The Readiness Assessment

A 5-day engagement that maps your data, surfaces high-ROI AI candidates, and recommends a pilot — fixed price.

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Case study
Tier-1 bank cuts reconciliation 92%

Agentic reconciliation across 14 source systems — six-week pilot, full rollout in one quarter.

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New
Private AI on dedicated GPUs

Frontier-class models on isolated infrastructure — your data never leaves the perimeter.

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Latest
Field notes: agentic eval at production scale

How we ship and operate eval harnesses for systems running ten-million-plus actions a month.

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Founder
Rohit Wakode — Founder & Director

B.Tech IIT Bombay · LLB GLC Mumbai. Building intelligent enterprise systems in India since 2014.

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Solution · 01

Agentic AI Systems

Autonomous workflows that read, reason, and write across your operational stack.

LangGraphTemporalHITLTool-use

Most “AI agents” are demos that fall over the moment they touch a real system of record. We build agents the way you would build any mission-critical software: as stateful, observable graphs with explicit guarantees — not a prompt and a prayer.

An agent reads from your tools, reasons over policy and data, and writes back through validated actions. Every step is logged, every tool call is schema-checked, and any action above a risk threshold pauses for human approval. The result is automation that survives an audit, not just a sprint demo.

WhatAutonomous multi-step agents that act across your tools — with humans in the loop where stakes are high.
Best forHigh-volume operational work: finance, ops, IT, customer service.
RunsIn your VPC or on-prem, against your systems of record.
Time to valueWorking pilot in 6 weeks.
01 — Capabilities

What we build.

Specific, production-grade capability — not a feature checklist.

/ 01

Stateful agent graphs

Long-running workflows modelled as explicit state machines (LangGraph / custom) — resumable, inspectable, and deterministic where it matters.

/ 02

Validated tool-use

Every tool call is schema-checked and permission-scoped; the agent can only do what its role allows.

/ 03

Human-in-the-loop

Configurable approval gates for high-stakes actions — refunds, postings, external comms — with full context for the approver.

/ 04

Evaluation harness

Each action is graded against ground truth; production traffic is gated by score thresholds and regression tests.

/ 05

Memory & context

Hybrid retrieval gives agents the right context without leaking data across tenants or roles.

/ 06

Observability

Per-run traces, replay, cost and latency dashboards — you can see exactly why an agent did what it did.

02 — How it works

From your problem to production.

01

Map the workflow

02

Build the graph

03

Evaluate against truth

04

Ship & operate

STEP 01

Map the workflow

We shadow the real process, document the decision points and the systems involved, and agree the success criteria and risk gates before any code is written.

STEP 02

Build the graph

The workflow becomes a stateful graph with typed tools, guardrails, and human-approval gates. We wire it to your systems of record in a sandbox.

STEP 03

Evaluate against truth

We grade the agent on historical cases, tune until it clears the threshold, and red-team it for prompt injection and edge cases.

STEP 04

Ship & operate

Production rollout behind feature flags, with traces, alerting, and a senior engineer on call. You keep the runbooks.

03 — Where it pays

Use cases.

Treasury & bank reconciliationVendor / supplier onboardingInsurance claims triageIT operations & incident responseSales ops & quote-to-cashKYC / AML case handling
04 — Engineering

Stack & standards.

Orchestration
LangGraph
Temporal
Custom state machines
MCP tool servers
Models
Open-weight (Llama, Qwen, DeepSeek)
Commercial APIs
Model-agnostic routing
Reliability
Eval harness
OpenTelemetry traces
Guardrails / validators
HITL approvals
05 — Outcomes

What good looks like.

Hours
Operational time returned
Repetitive case work moves from people to supervised agents.
Audit-ready
Every action traced
Full lineage and replay for regulators and internal audit.
Weeks
To first production agent
Fixed-price pilot, not an open-ended research project.
06 — Questions

Answers, before you ask.

How is this different from RPA?
RPA follows brittle scripts and breaks when a screen changes. Our agents reason over goals and data, validate every action, and degrade safely — with human approval where it matters.
What stops an agent doing something dangerous?
Permission-scoped tools, schema validation on every action, and hard approval gates above a configurable risk threshold. Agents physically cannot call tools outside their role.
Do our data and prompts leave our environment?
No. We deploy inside your VPC or on-prem, with open-weight models where required, so data never crosses your boundary.
Ready when you are

Put Agentic AI Systems into production.

Start with a fixed-price 5-day Readiness Assessment or a 6-week pilot. Senior engineers, measurable evals, and a system you own on handover.

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