Agentic AI services
Agentic AI services for enterprise.
Agents in production, not pilots.
We design, build, and operate production agentic AI systems — computer vision across 100,000+ SKUs in 35 countries, research agents collapsing 3-day analyses to an hour, managed services automation with 90%+ effort reduction.
Built for Indian regulatory compliance from the architecture phase.
Trusted by
Selected work
Agents we've shipped, outcomes we've measured
Three production deployments across supply chain, risk intelligence, and managed services.
Reduction in technology support effort
Orchestrik deployment — managed services
We built and deployed Orchestrik agents into our own managed services operation — triaging incidents, executing routine remediation, and escalating with full context. Support effort reduced by over 90%.
See OrchestrikReduction in SKU identification time
Custom computer vision — global supply chain leader
Computer vision system identifying 100,000+ SKUs across 35 countries. Sub-3-minute lookup per item, built as a custom convolutional neural network with serverless inference — running in production.
Supply chain workThird-party risk analysis turnaround
Research agent — global intelligence and risk consulting firm
Four AI models orchestrated to automate third-party risk reports — document discovery, financial analysis, regulatory checks, and synthesis. Analyses that took 3 days now complete in under an hour.
Case studyWhat is agentic AI
Agentic AI vs RPA vs traditional ML — one screen
Most misbuilt AI projects fail at this distinction. Here is where each category stops.
| RPA / Rule-based | Traditional ML | Agentic AI | |
|---|---|---|---|
| Trigger | Fixed rule or schedule | Batch data or API call | Monitors context, self-triggers on goal state |
| Decision-making | Pre-defined decision tree | Statistical model output | Multi-step reasoning across tools and data sources |
| Adaptability | Breaks on new inputs — needs reprogramming | Retrain required for distribution shift | In-context adaptation; escalates novel cases to humans |
| Failure handling | Error + halt | Silent degradation | Audit trail, rollback, human escalation paths built in |
Agentic AI systems combine an LLM reasoning engine with access to tools, APIs, and data sources. The agent plans a sequence of steps toward a goal, executes them, checks its own output, and iterates — without a human prompting each step. What distinguishes production deployments from demos is test coverage, observability, audit trails, and rollback paths built in from the start.
What we deploy
Agentic AI use cases by industry
Named workflow types we build and operate in production. Each industry has its own regulatory posture, data integration surface, and failure modes.
Banking & Fintech
- Compliance monitoring — RBI Digital Lending, SEBI
- Fraud detection on transaction streams
- Tech-stack audit agents for due diligence
- Portfolio analytics for capital markets
Supply Chain & Logistics
- Computer vision for SKU identification at scale
- Multi-site inventory reconciliation
- Demand forecasting agents
- ERP integration — Dynamics 365, SAP
Insurance
- AI underwriting decision support
- Claims automation
- IRDAI compliance + IIB data reporting
- Cloud-native core insurance infrastructure
Life Sciences
- Formulation assistants — CDSCO + FDA 21 CFR Part 11
- Regulatory document automation
- R&D research agents
- Clinical and research data pipelines
Retail & D2C
- Personalisation engines
- Demand forecasting and inventory agents
- Returns and fraud detection
- Last-mile delivery optimisation
Hospitality
- Guest concierge agents
- Revenue management agents
- Ops and maintenance routing
- Booking-system orchestration across channels
Professional Services & Accounting
- Month-end close reconciliation agents
- Audit working-paper preparation
- Tax research — Indian GST + global
- Engagement-letter drafting and review
Quick Commerce
- Demand-forecasting on 10-minute delivery SKUs
- Dark-store inventory rebalancing
- Dynamic-pricing for hyperlocal demand
- Delivery-failure triage agents
How we architect agents
Four deployment patterns — and when to use each
Picking the wrong agent architecture is the most common source of failed agentic AI projects. Here is how we evaluate each pattern before the build starts.
Single-agent + tools
Multi-agent orchestration
Agentic RAG
Decision-support + HITL
What production means
Production agents need more than a prompt
Most agent demos are not production systems. The gap between a working demo and a monitored, compliant, rollback-capable production deployment is where most agent projects fail. Here is what we build in from the start.
Deterministic test harness
Non-deterministic agents are tested against golden-trace replays and expected-outcome distributions before go-live. Every agent has a test suite that validates the decision path, not just the final output.
Observability + tracing
Every agent call captures span, latency, token count, tool calls made, and intermediate reasoning steps. We instrument before go-live, not after an incident.
Audit trail
Regulator-grade log of inputs, outputs, and decisions — retained per the applicable framework: RBI, IRDAI, FDA 21 CFR Part 11. Not optional in regulated industries.
Rollback paths
Every production agent can be disabled or rolled back to the last known-good configuration without disrupting the underlying operational system.
Non-determinism monitoring
Drift detection against expected outcome distributions. Alerts fire when behaviour shifts outside acceptable bounds — treated as a production incident, not an accepted limitation.
How we build
A three-step engagement model
No open-ended retainers. Every engagement starts bounded, ships production-grade, and includes a warranty period.
Architecture sprint
Two weeks, fixed price. We identify the right agent pattern — single-agent, multi-agent orchestration, or hybrid with human-in-loop. We scope data access, integration surface, and failure modes before writing a line of agent code.
Production build
Agents that operate inside your infrastructure — ERP, SaaS, legacy APIs — with monitoring, audit trails, and rollback paths. Production-grade code, not a prototype. Tested for non-deterministic behaviour before go-live.
Operate & improve
Post-go-live monitoring with edge cases logged and models updated. Non-deterministic behaviour tracked against expected outcomes. 4–8 week warranty period included.
Choosing a provider
How to evaluate an agentic AI services company
Most agentic AI service providers can demo an agent. Far fewer can show you one still running in production six months later. Six questions that separate the two.
“Show me an agent running in production today.”
A demo proves a prompt works. Production proves the integration, the error handling, and the monitoring work. Ask for the deployment date and who operates it now.
Ours: Computer vision across 100,000+ SKUs in 35 countries, a four-model research agent for a global risk consulting firm, and Orchestrik agents running inside our own managed services operation.
“Which agent pattern would you use, and why not the other three?”
A provider who names an architecture before understanding your workflow is selling what they already built. The pattern should follow the failure modes, not the sales deck.
Ours: We choose between single-agent with tools, multi-agent orchestration, agentic RAG, and decision-support with a human in the loop during the architecture sprint, and we write down why the others were rejected.
“What happens when the agent is wrong?”
Every agent is wrong sometimes. The question is whether that is detected, logged, reversible, and escalated to a person, or whether it quietly corrupts a workflow for a week.
Ours: Audit trails, rollback paths, and human escalation ship in the first release, not after the first incident. Non-deterministic behaviour is monitored against expected outcome distributions.
“Who owns the regulatory posture?”
Where RBI, SEBI, IRDAI, CDSCO, or DPDP exposure exists, compliance is an architecture decision. A provider who treats it as a review at the end will need to rebuild.
Ours: The architecture sprint produces a compliance posture document before the production build starts.
“How is the engagement bounded and priced?”
Open-ended retainers transfer all the schedule risk to you. You should know the shape of the commitment before the first invoice.
Ours: A two-week fixed-price architecture sprint comes first. The production build is scoped and priced after it, so you know the full cost before committing to it.
“What happens after go-live?”
Handover without observability tooling and runbooks is abandonment. Ask what is covered, for how long, and at whose cost.
Ours: A 4 to 8 week warranty on every engagement, covering edge-case triage, model and prompt updates, and rollback if a regression appears. After that, managed operations or a full handover.
Regulatory posture
Compliance built in from architecture, not retrofitted
Generic AI platforms cannot carry Indian regulatory context out of the box. We architect for it from the first sprint — and where security exposure is the primary concern, we start with a cybersecurity risk assessment.
RBI Digital Lending + SEBI
Fintech agent deployments aligned with RBI Digital Lending Guidelines and SEBI cybersecurity circulars from the architecture phase, not retrofit.
DPDP consent + data residency
Indian enterprise agent deployments built around DPDP consent requirements and Indian data residency from the outset.
IRDAI digital transformation
Insurance agent workflows designed against IRDAI directives and IIB data reporting mandates.
FDA 21 CFR Part 11 / CDSCO
Life sciences automation built for FDA Part 11 audit trails and CDSCO compliance requirements.
Public sector data sovereignty
Government and public-sector deployments architected for data residency, classification handling, and audit posture.
For a different intent
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Readiness assessment
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Agentic AI — depth by intent
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