Discover & Scope
Align on problems, data reality, and success metrics. Opportunity brief, KPI model, phased roadmap, effort/cost ranges.
LLM & AI Agent
Development
Agents that decompose a goal, call your real systems, recover from failure, and escalate to a person when they are not sure. With a full trace of every decision.
Agents that do the work, not just describe it
Designing AI-powered products requires more than A chatbot answers. An agent acts. The difference is everything, and it is also where most implementations fall over — because acting means touching real systems, and real systems punish mistakes. We build agentic systems the way you would build any other critical software: with typed interfaces, schema validation, retries, rate limits, permission checks and an audit trail on every single tool call. The planner decomposes intent into an explicit, reviewable plan with pre- and post-conditions per step. The executor calls your APIs inside a sandbox. A policy layer sits between the two and decides what the agent may do on its own and what needs a human.
That policy layer is the part everyone skips and the part that makes the difference. Confidence gates route low-certainty steps to a human approval inbox with one-click approve, edit or reject — so the agent is aggressive where it is sure and cautious where it is not. Every run is fully traced and replayable. When something goes wrong, you do not get a shrug; you get the exact prompt, the exact tool call and the exact decision that caused it. That is what makes an agent something a compliance team will actually sign off on.
What We Do?
We design and build domain-specific agents and copilots: planner-executor runtimes, retrieval-grounded assistants, and multi-step automations that replace brittle RPA. We wire them into the tools you already run — CRM, helpdesk, ERP, internal APIs — over REST and MCP connectors with schema validation and guardrails.
We build the human-in-the-loop layer that decides when to escalate. And we instrument the whole thing with OpenTelemetry, so every run can be replayed, audited and explained.
Key Deliverables
A production agent runtime that does real work inside your stack, with guardrails, approvals and a full audit trail — not a demo that hallucinates its way through a happy path.
- Multi-step planning that compiles intent into a typed, reviewable plan
- Tool use and function calling against your existing APIs, sandboxed and rate-limited
- Confidence-gated human-in-the-loop approvals for anything uncertain or sensitive
- Full run traces — every prompt, tool call and decision logged for audit and replay
What's included ?
- +Agent architecture and planner design
- +Tool and API integration layer
- + Guardrails, policy gates and approval workflows
- + Tracing, evaluation and replay infrastructure
Process
From Idea
to Production
to Production
FAQs
Frequently
asked questions
asked questions
A focused pilot reaches a working, testable v1 in four to six weeks. A production system - with on-site deployment and integration-typically runs eight to twelve weeks depending on hardware, data access and how many systems it has to talk to. We tell you which one you are in during discovery, not after.
A clear problem statement, a definition of success, access to sample data, and one stakeholder who can make decisions. That is genuinely it. We run a kickoff workshop to pin down scope and the KPI model before anyone writes code.
Whichever ones wins on accuracy, latency and cost for your problem. In practice: Claude and GPT for reasoning and generation; Llama and Mistral when it has to be self-hosted; YOLO, OpenCV and InsightFace for vision; PyTorch, ONNX and TensorRT for anything on the edge. We are not loyal to a vendor. We are loyal to the benchmark
Yes, and several of our systems do. We have shipped fully on-premise vision analytics that runs a single executable with no internet connection, and self-hosted voice assistants where every inference endpoint stays inside the customer VM. If compliance rules out third-party APIs, we design for that from the start.
Development is included in the project price. Model and API usage is billed at cost, based on your actual volume. We estimate it up front and then work to bring it down - compression, caching and smaller models where a smaller model is enough.
Monitoring, tuning and one support loop that runs from the engineers who built it. AI system drift-data changes, storefronts change, We watch for it and we fix it.