Services

AI agents & LLM applications

Assistants, agents and AI features built on large language models, taken from prototype to software your team relies on.

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What it is

What this service covers

We design and build software around large language models: assistants that answer from your own documents, agents that take actions in the tools your team already uses, and AI features inside products you already run.

The model is the smaller part of the work. Most of it is the software around it: getting the right data to the model, connecting it safely to your systems, testing its answers and watching how it behaves once real people use it.

Who it's for

  • Product teams adding an AI feature to an existing web or SaaS product
  • Companies that want staff or customers to get answers from internal documents and knowledge bases
  • Operations teams with repetitive work spread across email, CRMs, tickets and spreadsheets
  • Founders who have an AI prototype and need it to hold up in production

Scope

What we deliver

  • Knowledge assistants (RAG)

    Assistants that answer from your documents, wikis and databases, show where an answer came from and respect who is allowed to see what.

  • Agents that act in your tools

    Agents that read and update your CRM, ticketing, email or internal APIs through defined tool calls, with human approval where a mistake would be costly.

  • AI features in existing products

    Summaries, search, drafting, extraction and chat added to your product, with the backend, interface and access control that go with them.

  • Evaluation & monitoring

    Test sets that check answer quality before each release, plus logging, cost tracking and alerts once the system is live.

Tools we work with

  • OpenAI models
  • Anthropic Claude
  • Google Gemini
  • LangChain
  • Python
  • Django
  • FastAPI
  • PostgreSQL
  • Redis
  • Next.js
  • Docker
  • AWS

Process

How we work

  1. Discovery

    We look at the task, your data and the systems involved, agree what a good answer or action looks like, and write down scope, risks and a plan.

  2. Prototype

    A working version on your real data with a first evaluation set, so you can judge quality before committing to a full build.

  3. Build & integrate

    We turn the prototype into production software: integrations, permissions, interface, tests and deployment, shown to you in regular demos.

  4. Launch & monitor

    We release in stages, watch quality, cost and usage, and keep improving prompts, retrieval and tools based on what real users do.

Work

Related work

Engagement

How an engagement looks

A fixed-scope discovery and prototype first, then a dedicated team that builds and iterates with you.

  • AI Feasibility Sprint

    For teams with an AI idea who want to know if it works before they build it.

    • One use case assessed on your data and systems (if you bring several ideas, we rank them first)
    • A small proof of concept on your real data, with an evaluation set
    • A written build plan: architecture, model options, running-cost estimate, risks and dated milestones
    • A readout with your team
    Length
    About 2 weeks
    Price
    Fixed price, agreed before we start

  • Prototype Audit

    For teams with an AI prototype who want to know what it takes to run it in production.

    • A review of the code and architecture
    • A list of the risks we find
    • A dated plan to get it to production
    Length
    About 1 week
    Price
    Fixed price, agreed before we start

  • Prototype-to-Production Sprint

    For teams with an AI prototype that has to become one feature people can rely on.

    • An evaluation set and a quality baseline
    • Access control and permissions
    • Deployment to your cloud
    • Monitoring, logging and cost tracking
    • Handover in your repository
    Length
    3–4 weeks
    Price
    Fixed price, agreed before we start
  • The AI Feasibility Sprint or Prototype Audit fee is credited in full if the build starts within 30 days of delivery.
  • Builds are billed per milestone. You can stop at any milestone and keep everything delivered.
Compare all ways to start

FAQ

Questions buyers ask us

Who owns the code?
You do. Once the work is paid for, the code, prompts and evaluation data we write for you belong to you and live in your repositories.
Will you sign an NDA?
Yes. We're happy to sign an NDA before you share any documents, data or system access.
How do you handle time zones and communication?
Our team works from Dhaka (UTC+6). We share the European morning every working day. For US clients we agree one fixed daily call time at kickoff. We share progress in writing, so decisions don't wait for the next meeting.
How do we get started?
Book a 30-min call. Within 1 business day we send a short written note on fit and a proposed first step, usually an AI Feasibility Sprint, a Prototype Audit or a Prototype-to-Production Sprint.
Which models and stack do you use?
We choose the model per task and test it on your data, usually from OpenAI, Anthropic (Claude) or Google (Gemini). We build in Python with Django or FastAPI, use LangChain where it helps, store data in PostgreSQL and Redis, build interfaces in Next.js and deploy with Docker on AWS or your own cloud.

Have a project in mind?

In a 30-minute call we talk through what you want to build, your data and your systems, and whether we're the right team for it.

Book a 30-min call(opens in a new tab)

What happens next

  1. A 30-minute call

    With an engineer, not a salesperson, about what you want to build.

  2. A written note within 1 business day

    A short note on whether we're a good fit and a proposed first step.

  3. An NDA before any data

    Signed before you share documents, data or system access.