Service · Applied AI systems

AI development services for startups
and enterprises.

Start a project
01Overview

We get judged on what survives contact with production, not on what the prototype could do.

Six things clients ask us to prove before a build starts. Every figure below is already carrying load in something we have shipped, and links to the write-up it came from.

02Proof

What we have actually shipped

Agents that qualify leads overnight, pipelines that publish without supervision, platforms that carry real money, and internal tooling that replaced a spreadsheet. Each one was designed for a specific workflow. Four of them are written up in full:

03Use cases

Five kinds of AI build, and what each one owns.

BuildWhat it ownsWhat a person keeps

Custom AI Agents

Read the inputs, reason through the steps, call the tools, and stop where a person needs to sign off.

The sign-off

Multi-Agent Pipelines

Split a long job across specialists that hand work down a chain, with a quality gate before anything leaves the system.

The publish bar

AI SaaS Platforms

Tenancy, billing, roles and the AI features themselves, built as one product rather than a model bolted onto a CRUD app.

The roadmap

Workflow Automation

The repetitive middle of a process: extract, classify, reconcile, route, and escalate whatever does not fit.

The exceptions

AI Inside an Existing Product

A feature layer on what you already run — document analysis, summaries, intelligent search — without a rewrite.

The product direction
Not on the list? Tell us the workflow
04How we build

How a build actually runs.

  1. 01ScopeWhat we agree before code

    The workflow mapped as it really happens, the success measure written down, and the parts that are explicitly out. Five-week deliveries are only possible because this is settled first.

  2. 02ArchitectureThe shape before the sprint

    Data model, tenancy boundaries and the tool surface, decided up front. Retrofitting isolation into a live product is the expensive kind of rework.

  3. 03Model layerProvider-agnostic by default

    The model sits behind our own interface, so swapping or adding one is a config change rather than a refactor. FlyCRM ships exactly this way.

  4. 04GuardrailsWhat it may never do

    Records it cannot touch, thresholds it cannot cross, and the actions that always need a person. Encoded before launch, not after an incident.

  5. 05ConfirmWhere a person says yes

    The point the system stops and hands over. On FlyCRM that line is absolute: no record reaches the database without it.

  6. 06HandoverWhat you own at the end

    Documentation, infrastructure access and a system your own team can extend. The build is not finished while it still needs us.

05Why Claude

Why We Build with Claude

Three reasons, each one a failure we would rather not spend the project debugging.

01

Instruction-following that survives a long chain

14 agents across 6 layers

A system that reads a 50-page report, extracts fields, classifies them and fires the right workflow fails at whichever step drifts first. Across the fleets on this site — 14 agents in one, 8 in another — the compounding matters more than any single-shot benchmark. Claude held the chain best in the comparisons we ran before committing.

02

A context window that removes the chunking layer

96% ruled out on one pass

Whole documents, full ticket histories and entire exports go in on one pass. That deletes the chunk-and-stitch code that is the usual source of dropped context — and the usual source of a summary that quietly omits the important paragraph.

03

Predictability we can put in front of a client

0 writes without a confirm

Every build here writes into someone's production system. Consistent, well-bounded behaviour is what makes a human-confirm gate meaningful and what gets a build through an internal review. It is the reason the FlyCRM figure is 0 and not 'low'.

Frequently Asked Questions.

What does an AI development company actually build?

An AI development company designs and builds software systems that use artificial intelligence to automate tasks, analyse data, and make decisions. This includes AI agents, machine learning pipelines, NLP-powered tools, SaaS platforms with AI features, and intelligent automation systems-all tailored to specific business problems.

Timelines vary by scope. A Claude-powered MVP can typically be designed, built, and launched in 3–6 weeks. More complex AI systems-such as multi-agent workflows or full AI SaaS platforms-usually take 2–4 months depending on integrations, data requirements, and testing cycles.

We primarily build with Anthropic's Claude as the core intelligence layer-chosen for its strong reasoning, instruction-following, and safety characteristics. We also work with OpenAI models, custom fine-tuned models, and open-source LLMs depending on the project requirements. On the infrastructure side, we use Python, Node.js, AWS, and PostgreSQL.

Both. We've worked with early-stage founders validating their first AI product and with established companies integrating AI into existing workflows. Our engagement model adapts to your stage from lean MVP builds to full-scale production systems with ongoing support.

Idea in your head? Let’s
bring it to life.

Got a project? A wild idea? Or just want to say hey?
We're here for all of it — reach out anytime.

I’m looking for a help with:

I’m hoping to stay around of (in USD):