An AI agent development company that ships agents you can put in front of customers
Most agent demos fall apart the first time a real user is vague. We build agentic AI with the unglamorous parts included: evals, tool-call guardrails, cost caps, fallbacks, and traces you can read when something goes wrong.
- Fixed price: scoping mistakes are ours to absorb
- You own the code and the evals
- Inference billed to your provider, never marked up
Why this is worth building, and why us.
An agent is easy to demo and hard to trust. Wire an LLM to a few tools, script a happy path, and it looks like magic in a recording. Then a customer types something ambiguous, the model calls the wrong tool with confident nonsense, and there is no trace explaining what happened and no test that would have caught it.
Our AI agent development services exist for the gap between those two states. We build agents that decide, call tools, and loop until a task is done, and we ship them with an eval suite that proves they still work after you change the prompt, cost caps that stop a runaway loop billing you, and observability that turns a bad answer into a fixable bug instead of a mystery.
What we build
- 01Customer-facing agents: support, qualification, booking, triage, with a human handoff that works
- 02Internal agents: research, data extraction, report generation, the work nobody wants to do twice
- 03RAG pipelines: retrieval over your documents that cites sources instead of confabulating them
- 04Multi-agent systems: orchestration, shared memory, and a supervisor that can stop the whole thing
- 05Eval suites: a regression test for behaviour, so a prompt change cannot silently break production
- 06Cost and latency dashboards; you see spend per conversation before finance does
Productized, because agents are scopeable
Agent work gets quoted vaguely because most agencies are guessing. We have shipped enough of them to price the common shapes up front, so you can compare us against a number rather than a proposal.
Not sure an agent is even the right answer? Two days, your real data, a narrow proof-of-concept, and an honest verdict: agent, plain automation, or not yet. You leave with a fixed build quote and a per-conversation run-cost estimate. Credited back in full against any build you start within 30 days.
- Single Agent$2,4972 weeks
One narrow job, done properly: the cheapest way to find out if agents work for you.
- One agent, one clearly scoped task, with tool-calling
- A starter eval set so you can tell if a change made it worse
- Cost caps and rate limits from day one
- Deployed to your infrastructure, code in your repository
- Extends into the Production tier rather than being thrown away
- Most chosenProduction Agent$4,9973 weeks
An agent you are putting in front of customers, with the safety work included.
- Everything in Single Agent, plus memory and a wider tool surface
- A full eval suite: the happy path and the ways it breaks
- Guardrails: input validation, output checks, and a refusal path
- Tracing and structured logging you can actually read
- Human handoff for anything the agent should not decide alone
- A live cost and latency dashboard
- Multi-Agent System$12,9976–8 weeks
Workflows where several agents coordinate, or retrieval has to be right at scale.
- Everything in Production Agent, across an orchestrated set of agents
- RAG over your document corpus, with citations and freshness handling
- Supervisor logic, shared state, and a kill switch
- Human-in-the-loop review for the steps that need a person
- Load testing and a cost model at your projected volume
- Thirty days of post-launch tuning included
- Fixed price: scoping mistakes are ours to absorb
- You own the code and the evals
- Inference billed to your provider, never marked up
- Cost caps from day one, so a loop cannot run up a bill
These prices assume you already have the data or the tools the agent needs to reach. If part of the job is building the API it calls, or cleaning the corpus it retrieves from, that is separate work and we will scope it plainly rather than absorb it into a vague number.
Evals are the product, not the paperwork
The thing that separates an agent you can ship from one you cannot is a suite of tests that describe how it should behave, including on the inputs that break it. Without that, every prompt change is a coin flip, and "it seemed better when I tried it" is your entire quality process.
We build the eval suite alongside the agent, not after it, and we hand it over with the code. It is the reason you can change a model, tighten a prompt, or add a tool six months from now and know within minutes whether you made things worse. We wrote up why we do this on every build in the lab.
LangGraph, LangChain, or neither
We build on LangGraph when a workflow genuinely needs explicit state, branching, and the ability to resume a run mid-flight; its graph model is the clearest way to express that, and the checkpointing is real. We use LangChain where its integrations save time, and we skip both entirely when a task is a single tool-call loop that a framework would only bury under abstraction.
The honest position: most agents that reach production are simpler than the framework discourse suggests. Direct SDK calls with a well-designed tool layer are easier to debug, cheaper to run, and easier for your team to inherit. We pick per project, we tell you why, and we do not charge you for complexity that exists to look impressive.
What an agent costs to run, not just to build
Build cost is the number everyone quotes. Run cost is the one that surprises people. An agent that loops five times per request, each with a large context window, can cost more per conversation than the support ticket it replaced, and you will not find out until the invoice lands.
We model this before we build: expected tokens per task, cache strategy, which steps can drop to a smaller model, and a hard cap that fails safe. You get a per-conversation cost estimate during the Feasibility Sprint and a live dashboard after launch. If the economics do not work, that is a finding worth $497.
When we tell you not to build an agent
If the task is deterministic, an agent is the expensive way to do it. A rules engine, a scheduled job, or a plain n8n workflow will be faster, cheaper, and it will not hallucinate. A surprising share of "we need an AI agent" briefs are really "we need this process automated", and the automation answer is both better and cheaper.
We would rather sell you a smaller thing that works. If the Feasibility Sprint concludes that plain automation is the right call, we will say so and point you at our automation work instead.
Not claims. Shipped work.
- 01AI Calling AgentOutbound AI voice operations in production: an agent doing real work against real phone calls.See the work
- 02ChatberryAn AI WhatsApp marketing platform, built and shipped as a product rather than a proof of concept.See the work
- 03Why we ship eval suites with every agentOur written position on the discipline that makes agent work trustworthy.See the work
Common questions.
01How much does AI agent development cost?
Ours are published: $2,497 for a single narrow agent in two weeks, $4,997 for a customer-facing production agent in three, $12,997 for an orchestrated multi-agent system. Across the market a single-purpose agent typically runs $5,000 to $25,000 and multi-agent systems $25,000 to $100,000, so we sit at or below the bottom of both bands. The $497 Feasibility Sprint converts any of them into an exact quote plus a run-cost estimate, and comes off the invoice if you go ahead.
02What is the difference between an AI agent and an automation?
An automation follows a fixed path you defined. An agent decides what to do next, which tool to call, whether to retry, when it is finished. Agents are worth the extra cost and unpredictability only when the task genuinely varies. When it does not, we will point you at automation instead, because it is cheaper and it cannot hallucinate.
03Do you use LangGraph or LangChain?
Both, when they earn their place. LangGraph is our pick when a workflow needs explicit state, branching, or the ability to resume mid-run. LangChain is useful for its integrations. For simple tool-call loops we skip frameworks entirely and use the provider SDK directly, because it is easier to debug and cheaper to run. We choose per project and explain the call.
04Who pays for the model usage?
You do, billed directly by your provider on your own account. We never resell inference or take a margin on tokens. During the Feasibility Sprint we model expected cost per task so you know the running number before you commit to the build.
05What stops the agent doing something harmful or expensive?
Guardrails on the way in and out, tool-call allowlists, cost caps and rate limits that fail safe, and a human-in-the-loop step on any action that is hard to reverse. The eval suite covers the adversarial cases as well as the happy path, so you can prove the constraints hold before a customer finds the gap.
06Can you take over an agent someone else built?
Often, yes. The usual first step is a short audit: we write evals against the existing behaviour so we can see what actually works before changing anything, then fix from there. If the honest answer is that a rebuild costs less than a rescue, we will show you the reasoning rather than bill you for the slower path.
Find out if an agent is the right answer
Tell us the task you want an agent to take over. We will come back with whether it needs an agent or just automation, what it would cost to build, and what it would cost to run.
- A real person replies, usually same day
- No sales sequence, no automated nurture
- Feasibility Sprint is credited back if you go ahead