AI Solutions
From LLM integrations and assistants to RAG over your own data and agents that finish multi-step tasks. One senior team, with the guardrails and evaluation to run it in production.
What we build
Pick one, or combine them. Every solution starts from a real workflow and a way to measure whether it worked.
Add language models to the workflows you already run: drafting, summarising, classifying, extracting and routing, wired into your existing systems and permissions.
Typical use
Turning inbound emails and documents into structured records your team can act on.
Built with
Production-grade assistants for customers and teams, grounded in your content, with hand-off to a human when confidence is low.
Typical use
A support assistant that answers from your docs and escalates the cases it cannot close.
Built with
Retrieval-augmented generation pipelines that answer from your own knowledge base, with sources cited so people can check the answer.
Typical use
Search across contracts, tickets and wikis that returns answers with links to the source.
Built with
Autonomous agents that plan, call tools and complete multi-step tasks, with limits on what they may do and a trace of what they did.
Typical use
An agent that checks an order, applies your refund policy and drafts the reply.
Built with
Automations that combine AI steps with the tools your team already uses, so repetitive work runs in the background and exceptions reach a person.
Typical use
Lead enrichment, invoice processing and report generation running on a schedule.
Built with
When off-the-shelf does not fit, we design AI features around your domain, data and constraints, from first prototype to a supported product.
Typical use
A domain-specific copilot, scoring engine or analysis tool that becomes part of your product.
Built with
How an agent works
An agent is a loop, not a prompt. Follow one request through it: a customer asks for a refund.
1 / 5
The task and its limits are set up front: what the agent may do, and when it must stop and ask.
Resolve the refund request for order #4821, up to the auto-approve limit.
Built for production
Most AI projects fail after the prototype, in the messy middle of real data and real users. We build the safeguards in from the start.
Rules around what the system may say and do.
A test suite for AI behaviour, so quality is measured, not guessed.
You can see what the system did and why.
Your data stays under your control.
How we work
The same delivery rhythm as our MVP builds: a written plan, short cycles and a demo every Friday.
Pick the workflow, define what success looks like and how it will be measured.
A working version against real examples, so decisions rest on evidence.
Build the test set, add guardrails and fix the failure cases.
Integrate, deploy and monitor. You own the code, repo and infrastructure.
Track quality and cost, tune prompts and models, and extend what works.
In practice
AI / MVPNeuture AI
For Neuture AI, we helped turn the founders' concept into a functional, production-ready MVP designed to validate the product and support their next stage of growth.
Read case studyResultA validated MVP supporting fundraising and accelerator applications
Twenty-eight tools we run in production — models, agents, data and the plumbing that keeps them shipping.
Grok
GPT-5Grok
OpenAI Agents SDKpgvectorPinecone
BullMQ
OpenAI Agents SDKpgvectorPineconeBullMQStartups we build with have raisedup to $2M in funding
In work that is repetitive, text-heavy or slow because people have to search for information: support, document handling, internal knowledge, data entry, research and reporting. We start by finding the one workflow where the gain is clear and measurable, rather than adding AI everywhere.
We are not tied to one provider. We build with GPT, Claude and Gemini, and with open models such as DeepSeek and Qwen when cost, privacy or control matter more. The architecture keeps the model swappable, so you are not locked in when a better one appears.
Usually yes. Depending on your requirements we can use private endpoints, redact sensitive fields before they reach a model, or run open models in your own environment. We agree the data flow with you before building, and you own the resulting code and infrastructure.
We ground answers in your data and cite sources, add guardrails around what the system may do, and build an evaluation set from your real cases so every change is tested. Low-confidence or high-risk cases go to a person. No AI system is perfect, so we design for the failures too.
A focused AI feature or agent can often be prototyped quickly, an MVP ships in about a week, and the production-ready V1 typically takes around 12 weeks. We build with agentic systems ourselves: custom AI agents and automated workflows speed up our delivery. We do not publish fixed prices because scope decides the cost. Answer a few questions in our Project Scoper and we will send a quote by email.
Yes. Most of our AI work is integration into existing products and workflows: connecting to your data, APIs and permissions without a rebuild. If the codebase needs stabilising first, our consulting and audit work covers that.
We partner with startups, scale-ups and established businesses across industries, from fintech and SaaS to AI and trading platforms. Our client portfolio reflects the trust and long-term collaboration we build by shipping production-ready products.
Next step
Tell us the workflow you want to change. A senior engineer looks at it before the call, so we talk about your problem, not a pitch.
We solve business problems, take a consultative approach to every client engagement, and find actionable solutions that will help your organization.
