Custom AI agents
Multi-step agents on Claude, GPT-4o and open-source models. Tool-use, planning, memory and safe action, wired into your CRM, helpdesk and internal tools.
Custom AI agents, RAG copilots, document AI and voice automation. Built on Claude, GPT-4o and open-source models, with evaluations, guardrails and observability from day one.
Agents that act, copilots that ground, document AI that scales, engineered for reliability, not demos.
Multi-step agents on Claude, GPT-4o and open-source models. Tool-use, planning, memory and safe action, wired into your CRM, helpdesk and internal tools.
Retrieval-augmented copilots over your docs, tickets, CRM and code. Pinecone, Weaviate, pgvector and Anthropic citations, answers that ground in your data.
Customer-facing assistants with intent routing, hand-off to humans and CSAT loops. Reduce ticket volume; lift first-response and resolution times.
Lead qualification, call summarisation, sequence drafting, account research and pipeline triage. Plays back into HubSpot, Salesforce and Linear.
Invoice extraction, contract review, KYC, claims triage and bulk classification. Structured JSON outputs, validation and human-in-the-loop review.
Inspection, OCR, defect detection, identity verification and asset tagging. Built with Claude Vision, GPT-4 Vision, Roboflow and bespoke models.
Real-time voice agents, transcription, summarisation and analytics. Deepgram, ElevenLabs, OpenAI Whisper and Twilio for voice, integrated into your phone tree.
Turn meeting notes, calls, emails and PDFs into structured insights, summaries and action items, streamed straight into Notion, Linear or your CRM.
Custom fine-tunes on OpenAI, Mistral and Llama for narrow tasks where prompt engineering plateaus. Includes data curation, eval harness and rollback plan.
LLM evaluation harnesses, regression tests and live monitoring. Langfuse, Phoenix, Braintrust and Helicone, so you actually know if a prompt change made things worse.
Prompt-injection defences, PII redaction, output validation, refusal policies and role-aware access. Audit trails on every agent we ship.
Use-case ranking, build-vs-buy decisions, model selection, cost modelling and 90-day rollout plans. Practical, prioritised, board-ready.
We pick the model your use case actually needs, and put eval harnesses in front of it before anything ships.
Every prompt and agent ships with a benchmark suite. Quality regressions are caught before they reach your customers, and improvements are measurable, not anecdotal.
Retrieval-grounded prompting with citations is the default. Your model quotes your data, and refuses to confabulate when retrieval fails.
Agents propose; humans approve, until trust is earned through evals and live data. Cost caps and audit trails on every agent. Reversible blast radius.
Code, prompts, eval datasets, vector stores and provider accounts, all yours. No proprietary platforms you can't leave when a better model ships.
We've shipped production AI for hosts, retailers, plumbers, lawyers, hospitals and SaaS founders. The shape changes, the rigour doesn't.
We’re model-agnostic. We default to Anthropic Claude for reasoning-heavy work and OpenAI GPT-4o for tool-use and multimodal, with Gemini, Mistral and Llama in the mix where they’re a better fit. The right model depends on your latency, cost, privacy and quality bar, we benchmark a shortlist for each use case before we commit.
Business Automation is for workflow plumbing, Zapier, Make, n8n, HubSpot integrations, ETL pipelines. AI & Automation is for AI-first capabilities, agents, RAG copilots, document AI, voice and vision. They often work together: an automation triggers an agent, the agent updates a CRM, the workflow continues. Many clients buy both.
Real actions, that’s the point. Agents call tools (your APIs, CRMs, helpdesks), draft and send messages, update records, generate documents, route tickets and trigger downstream workflows. We always start human-in-the-loop and graduate to autonomous only where evals justify it.
Three layers. (1) Retrieval-grounded prompting with citations so the model quotes your data, not its training set. (2) Output validators (regex, JSON schema, classifier) that reject malformed responses. (3) Eval harnesses (Langfuse, Braintrust) that test every prompt change against a benchmark suite before deploy.
Standard practice: enterprise endpoints (Anthropic, OpenAI Enterprise, Azure OpenAI) with no-training and zero-retention contracts; PII redaction before model calls where appropriate; on-prem or VPC inference for highly sensitive data; full audit trails. We work to GDPR, SOC 2 and your sector requirements.
Discovery sprints from £4,000. Production builds are scoped per use case and typically run £15,000 to £60,000 (chatbots, RAG copilots, document AI). Multi-agent or fine-tuned systems run higher. Every quote is fixed, itemised and tied to specific evals and KPIs.
Yes, but only when it’s the right answer. Most use cases are better served by retrieval + good prompting; fine-tuning shines for narrow style, format or low-latency tasks. We’ll tell you honestly which lever to pull, then run the data curation, training and eval cycle if it’s warranted.
We design for swappability. Provider abstractions, eval suites and monitoring make it straightforward to A/B a new model, validate on your benchmark and roll forward. Quarterly model refresh is included in our care plan.
You do. We build inside your provider accounts, hand over the Git repository, prompt library, eval datasets and observability tooling. No proprietary frameworks you can’t replace.
London, UK. We work with clients across the UK, EU and North America. Discovery and weekly demos run remotely or in person depending on what suits you.
Send a brief, a use case or just a problem to solve. We'll come back within one working day with a clear next step.