We build AI agents that actually work in production. From single-agent tools to complex multi-agent systems — architected, built, and shipped for startup founders.
AI agents that resolve tickets, answer questions, and escalate to humans when needed. RAG-powered, context-aware, and always available.
Orchestrated agent teams where each agent specializes in a task — research, writing, analysis, execution — working together autonomously.
AI agents that automate business processes — data extraction, document processing, email handling, report generation.
Agents that crawl data sources, analyze information, generate insights, and deliver structured reports on demand.
AI-powered outreach agents that qualify leads, personalize messages, and schedule meetings with prospects.
Agents that assist with code review, testing, deployment automation, and infrastructure monitoring.
Built a LangChain-powered multi-agent system with RAG for a non-technical founder. The system autonomously resolves 68% of support tickets. The founder raised $500K pre-seed on this product.
68%
Auto-resolved
50
Beta users (2 wks)
6 weeks
Build time
Four to eight weeks, depending on how much the agent has to reach into. Every phase ends with something that changes hands, so you can tell whether it is going well without taking our word for it.
We define what the agent must do and — more importantly — what it must never do. You get an evaluation set of 30–80 real cases drawn from your actual tickets, documents, or workflows, plus a throwaway spike that proves the hardest step is possible before anyone builds around it.
You get: Eval set · architecture decision record · risk list
Tool design is where agent quality is won or lost — the model chooses what to call based on the function name, the docstring and the argument schema, and no amount of prompting rescues a badly described tool. We build the loop, wire retrieval, and run it against the eval set every day so regressions surface the day they appear.
You get: Working agent · tool suite · retrieval layer
Production agents fail in ways demos never do: a tool times out, a document is malformed, a loop will not terminate. We add checkpointing so a run survives a restart, iteration caps so it cannot burn your API budget overnight, and human-in-the-loop gates on anything that moves money or touches a customer.
You get: Checkpointed state · escalation paths · cost ceilings
Every run is traced end to end — inputs, tool calls, retrieved context, outputs, cost. When the agent does something surprising in month three, you can answer why without guessing. This is the part most teams retrofit, and retrofitting it is considerably more expensive than building it in.
You get: Tracing dashboards · cost per run · quality metrics
We ship to your infrastructure under your accounts, run a supervised period against live traffic, and hand over the repository, the eval set, the runbook and the architecture notes. You own all of it outright.
You get: Deployed system · runbook · full IP transfer
We have written at length about the parts founders ask about most — how to choose between orchestration patterns, why agent memory degrades over long runs, and what a production security posture actually requires.
72%
ticket deflection
A support agent handling tier-one volume for a B2B SaaS platform, with clean escalation on everything it could not resolve confidently.
$500K
pre-seed raised
An AI customer support agent built as the technical centrepiece of a founder's raise — shipped, demoed, and funded.
4–8 wks
idea to production
The delivery window for most agent builds, from first scoping call to a system running against real traffic.
100%
IP ownership
Code, prompts, eval sets and infrastructure are yours. No licensing, no lock-in, no per-seat fee on your own product.
We primarily use LangChain, LangGraph, and custom agent frameworks. For LLMs: GPT-4, Claude, and open-source models. For memory and retrieval: Pinecone, Weaviate, ChromaDB. We pick the right tools for your specific use case.
Most teams reach for multi-agent architectures too early. A single agent with three to five well-scoped tools beats a three-node graph that reimplements the same loop with more latency and more places to fail. Orchestration earns its complexity when you need genuine branching, parallel fan-out, durable resumption after a crash, or a step-by-step audit trail. We will tell you when you do not need it.
It depends on token volume, model choice and how tightly the loop is bounded — but it is knowable in advance, and we model it during scoping rather than after launch. We instrument cost per run from week four, set hard iteration caps so a runaway loop cannot produce a surprise invoice, and route cheap sub-tasks to smaller models where quality allows.
Layered, not hoped for. Tools are scoped so the agent cannot reach systems it has no business touching, anything irreversible or customer-facing sits behind a human approval gate, execution runs sandboxed with explicit network egress rules, and every action is logged. We also test adversarially — poisoned tool output, malicious instructions inside retrieved documents, attempts to escape the sandbox.
You do, completely. The repository, prompts, evaluation sets and infrastructure configuration are transferred at handover. We do not retain a licence, we do not charge per seat, and nothing you paid us to build stays behind a wall we control.
That is a common starting point, and it is usually one of three things: tool descriptions the model cannot act on, no evaluation set so nobody can tell whether changes help, or missing state so every failure restarts from zero. We run a two-week diagnostic that identifies which, then fix it — rebuilding only what genuinely needs rebuilding.
Simple single-agent tools take 2-3 weeks. Multi-agent systems with RAG, memory, and tool use take 4-8 weeks. Complex autonomous workflows may take 6-10 weeks.
Yes. We build agents that integrate with APIs, databases, CRMs, email systems, Slack, and any platform with an API. Custom tool-use is a core part of agent architecture.
We build with production reliability in mind — error handling, fallback logic, human-in-the-loop escalation, monitoring, and logging. Our agents are built to fail gracefully, not silently.
Book a free discovery call. We'll map out the agent architecture and give you a clear timeline and estimate.
Book a Discovery Call