AI built into your product: RAG search and agents that use your APIs.

We add retrieval-augmented search and chat agents to software you already run, for B2B products and internal teams. Answers come from your own data, actions go through your own APIs, and the hard cases go to a person.

What we build

RAG over your data

Search and question answering over documents, catalogs, and knowledge bases, with re-ranking so the right source comes first.

Chat agents for B2B products

Agents your customers or staff talk to, built on function calling over your APIs, so they look things up and take actions instead of only replying.

Escalation and guardrails

Confidence-based handoff to a person, tools scoped to what the agent is allowed to do, and a log of what it did and why.

Fits what you already run

Your Postgres, your APIs, your queues. We fit the AI into the system instead of rebuilding the system around it.

Stack, and why

Python, FastAPI and Django

The service layer around model calls, retrieval, and tools.

Postgres with pgvector

Vectors stored next to your relational data, so there is one database to run.

LLM APIs (OpenAI, Cohere rerank)

Picked per task for answer quality, cost, and latency.

RabbitMQ and Celery

Indexing and long-running agent work kept off the request path.

Proof, not promises

AI Support Agent · E-commerce60% faster support response with an AI agentA conversational agent with tool calling that handles support chats and return requests end to end.Read the case study →RAG Search · Retail+25% search conversion with semantic catalog searchRetrieval-augmented search and recommendations across a large retail product catalog.Read the case study →

B2B AIRAG systems and chat agents for B2B clientsRetrieval over client documents and chat agents built into B2B products, beyond the case studies we can publish. Client names withheld.

RAGChat agentsPythonPostgres

How a project runs

1
Call
A short call about what needs to be built or connected, and why it matters to your product.
2
Discovery sprint
We map your systems, the external APIs, and the risks, then give you a range of hours.
3
Build in short iterations
Working pieces in your environment as we go, not a big reveal at the end.
4
Launch and after
Release with monitoring, then a handover to your team or ongoing support on the Scale tier.

How we bill

Hourly or monthly. Never a fixed price per project.

Hourly or a monthly block

You pay for the hours we work, or a fixed monthly amount for a reserved block of hours. Either way, you can scale up or down between months.

Why no fixed project price

Integrations always hide surprises: an undocumented rate limit, a webhook that fires twice, data that does not match between systems. A fixed price means either padding it for every risk or cutting corners when one shows up. We estimate a range of hours instead and revise it as we learn.

Starting small

A short call, then a discovery sprint to map your systems, the third-party services, and the risks before anyone commits to an estimate.

Engagement tiers

FeatureStarterGrowthScale

Discovery sprint✓✓✓

Dedicated engineer—✓✓

Production on-call——✓

Weekly engagement—20 hrs40+ hrs

FAQ

Do you train a custom model on our data?

Almost never. We use retrieval over your existing data with off-the-shelf LLMs. It ships faster, costs less to run, and stays current when your data changes, without retraining.

Can the agent take actions, not just answer?

Yes. Agents call your APIs through function calling, with permissions scoped per tool. Anything risky or low-confidence goes to a person instead.

How long does a first version take?

4–10 weeks for a first build is typical. Most of the variance comes from how ready your data and APIs are.

How do you price it?

Hourly or as a monthly block of hours. We do not quote a fixed price per project, because the surprises in AI and integration work show up after the start, not before.

Other services

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