Everyone has access to GPT and Claude. Nobody has a generative AI system designed for their specific workflows, compliance requirements, and proprietary data. That's what we build.
Discuss your generative AI needsYour team tried the ChatGPT API. It was impressive for 20 minutes. Then reality hit: it hallucinates facts about your products, it can't access your internal data, it has no guardrails for regulated industries, and nobody can tell you what it costs to run at scale. Enterprise generative AI isn't about prompting a model. It's about architecting a system around the model that makes it reliable, accurate, safe, and cost-effective.
Internal copilots for your sales team that draft proposals using your actual pricing and case studies. Legal review assistants that compare contracts against your standard terms. Engineering tools that search your codebase and documentation. We build applications where the LLM is the engine, but your data, your rules, and your workflows are the chassis.
Systems that don't just answer questions. They complete multi-step tasks. An agent that receives a customer inquiry, searches your knowledge base, checks inventory, drafts a response, and routes to a human only when confidence is low. We build autonomous workflows with clear boundaries, fallback logic, and audit trails at every step.
When your domain is specialised enough that general-purpose models underperform, we fine-tune models on your proprietary data. This improves accuracy, reduces hallucination on domain-specific topics, and can significantly reduce inference costs by using a smaller, specialised model instead of a massive general one.
Every production generative AI system needs safety infrastructure. We build hallucination detection, output validation against your business rules, content filtering, cost monitoring per request, latency tracking, and automated evaluation suites that continuously test model quality against your golden dataset. You know exactly how your system is performing at all times.
We don't do web apps on the side. Every engineer on your project has deep AI specialisation and has deployed production ML systems before.
We don't implement the first architecture that works. We explore options, test assumptions, and design the solution that fits your specific constraints.
Our AI-augmented methodology compresses delivery timelines by 2-3x compared to traditional consulting.
Timeline
6-12 weeks depending on integration complexity and data access
Team
2-3 senior engineers specialising in LLM systems
Deliverables
Production application, prompt engineering framework, evaluation suite, cost monitoring dashboard, security documentation, user training materials
After launch
Optional retainer for prompt optimisation, model updates as new LLMs release, and ongoing cost management
A professional services firm had 15 years of client engagement documents, proposals, and deliverables stored across SharePoint, Confluence, and email archives. New partners spent weeks searching for relevant precedents when scoping new engagements. We built a RAG-powered knowledge assistant: ingested and chunked 200,000+ documents, built a retrieval pipeline with hybrid search, connected it to an LLM with strict citation requirements, and deployed it as an internal web application with SSO integration. Partners now find relevant precedents in under a minute, with direct links to source documents. Proposal drafting time dropped by 60%.
Representative of a typical engagement.
Multiple layers. First, retrieval-augmented generation grounds the model in your actual data. Second, we implement citation requirements. Every claim must link to a source document. Third, we build confidence scoring so the system flags uncertain responses rather than guessing. Fourth, we run automated evaluation against a test set of known-good answers. And fifth, for critical applications, we add human-in-the-loop review before responses reach end users.
We're model-agnostic. We evaluate GPT-4, Claude, Gemini, Llama, Mistral, and others against your specific use case during the first sprint. Model selection depends on accuracy for your domain, latency requirements, cost per request, data privacy constraints, and whether on-premise deployment is required.
We model this during scoping and build cost monitoring into every deployment. Costs depend on request volume, model size, and response length. For many enterprise use cases, fine-tuning a smaller model on your domain delivers better accuracy at 10-20% of the cost of using a frontier model for every request.
Yes, that's usually the entire point. We build integrations with your databases, APIs, document stores, CRMs, ERPs, and any other system that holds data the AI needs to access. We also build tool-use capabilities so agents can take actions in your systems, not just read from them.
Tell us about your data, your workflows, and what you want the system to do. We'll design an architecture that fits.
Request consultation