We build autonomous agents that do real work inside your tools — triaging tickets, researching, and acting on your data — with human review where it matters. No AI theater, just agents that ship outcomes.
Agents that handle repetitive, rules-heavy work — ticket triage, data entry, report generation — freeing your team for judgment calls.
Agents grounded in your documents, tickets, and internal knowledge base via RAG — answering from your reality, not a generic model's guesses.
Every agent ships with clear escalation paths and approval gates, so autonomy never means losing oversight where it matters.
Agents that act inside the systems you already use — CRM, helpdesk, internal APIs — instead of yet another dashboard to check.
Seven steps, one continuous conversation — from your first brief to an agent live in production.
We listen first. The workflow, edge cases, and risk tolerance become a shared definition of what the agent should do.
Scope, data sources, and guardrails — a roadmap for what the agent handles alone and what it escalates.
Prompts, retrieval, and escalation paths prototyped and tested against real scenarios before production.
Sprint-based builds with working demos every two weeks, evaluated against real cases, not just happy paths.
Accuracy, safety, and edge-case testing — before the agent touches production data.
Staged rollout with monitoring and human review, so autonomy grows only as trust is earned.
Ongoing evaluation, prompt tuning, and monitoring — we stay on after launch day.
LLM Agents, RAG, LangChain, PyTorch, Vector DBs, Python — the technologies we use to build and ship.
Technical explainers and a real engagement — the kind of detail we'd want before making the same call ourselves.
Both change how an AI agent answers. Only one of them changes the model — here's how to tell which problem you actually have.
Read the guideChatbots answer; agents act. What actually separates the two, and how to pick the right one for the job.
Read the guidePrompt caching, batching, model routing, and context discipline — techniques that routinely cut LLM API spend by 60–90%.
Read the guideSupport volume was outpacing the team — an agent now handles the routine work, with clean human escalation for the rest.
Read the case studyEvery agent we build has explicit boundaries — actions it can take autonomously, and actions that require human approval — plus logging so every decision is traceable back to its reasoning.
Yes. We build retrieval (RAG) pipelines that keep your data in infrastructure you control, using models and vector stores scoped to your environment rather than sending your data to train someone else's model.
A focused, single-workflow agent (e.g. ticket triage) typically ships in 4-8 weeks, from scoping the workflow to production deployment with monitoring in place.
We build with model-agnostic architecture where practical, so swapping providers is a configuration change, not a rebuild — and we monitor pricing shifts so you're not caught off guard.
Yes, that's the usual starting point. Most agents we ship connect to a client's existing systems via API rather than requiring a new tool the team has to adopt.
We define success metrics upfront — resolution rate, escalation rate, time saved — and instrument the agent so you see real numbers in a dashboard, not just anecdotal impressions.