AI agents in production,
not pilots.

ITMTB has deployed a custom CNN for SKU identification across 100,000+ SKUs in 35 countries (97% reduction in ops time), a 4-model research agent for a global risk consulting firm (3 days to 1 hour for third-party analyses), and Orchestrik agents into managed services operations (over 90% reduction in technology support effort). All three are in production and actively monitored.

Compliance is built into the architecture phase, not retrofitted. We design fintech agents around RBI Digital Lending Guidelines and SEBI; insurance agents around IRDAI and IIB; life sciences automation for FDA 21 CFR Part 11 and CDSCO; and Indian enterprise deployments around DPDP consent and data residency. The architecture sprint produces a compliance posture document before the production build starts.

Engagements begin with a two-week fixed-price architecture sprint. From there, a bounded production agent typically ships in 6–14 weeks — simple single-agent deployments land closer to 6 weeks; multi-agent orchestration across enterprise systems is typically 10–14 weeks. Every engagement includes a 4–8 week warranty period post-handoff. The sprint is fixed-price; the production build is scoped and priced after the sprint.

ITMTB's Agentic AI Readiness Assessment identifies which workflows are ready, which need infrastructure work first, and what a bounded pilot looks like. The 3-minute quiz produces a structured report based on six readiness dimensions: data infrastructure, process maturity, technical compatibility, organisational alignment, regulatory posture, and change management.

Accounting involves highly structured, document-heavy, repetitive cognition — the ideal surface for agentic AI. We deploy agents for month-end close reconciliation (ingesting GL data, flagging discrepancies, producing a variance report), audit working-paper preparation, GST and international tax research, and engagement-letter drafting and review. The agent pattern is typically agentic RAG — retrieving your chart of accounts, prior-period files, and regulatory sources, then reasoning across them. Accuracy is verified against expected outputs before go-live.

A chatbot responds to a prompt and waits for the next one. An agentic AI agent takes a goal, plans a sequence of steps, calls tools and APIs, checks its own output, and iterates — autonomously, without prompting each step. The practical difference: a chatbot tells you what is in a contract; an agent reads the contract, queries three compliance databases, flags discrepancies against your template, and drafts a response — in one run. Agents use an LLM as a reasoning engine, not a chat interface.

The two-week architecture sprint produces a scoped build plan. From there, production timelines depend on integration complexity, not model complexity. A bounded single-agent deployment typically ships in 6 weeks from sprint completion. Multi-agent orchestration across enterprise systems (ERP, legacy APIs, multiple data sources) is typically 10–14 weeks. You know the full build cost before committing to the production phase.

Every engagement includes a 4–8 week post-go-live warranty. During this period we monitor agent behaviour against expected outcome distributions, triage production edge cases, update models or prompts as required, and cover rollback if a regression is detected. Non-deterministic behaviour caught post-launch is treated as a delivery issue, not a new engagement. After warranty, we offer ongoing managed operations or hand over with full observability tooling and runbooks.