GPT & LLM Integration Services
Embed real AI inside the tools your team already uses.
Bolt-on ChatGPT widgets don't move the needle. Properly engineered GPT and Claude integrations do — embedded inside your existing systems, grounded in your data, with the engineering layers that stop hallucination from breaking customer trust.
The pain points we keep hearing
- Generic ChatGPT doesn't fit your workflow. Off-the-shelf chat is fine for ad-hoc. It can't see your CRM, your knowledge base, your customer history — so the answers stay generic.
- You don't want to send everything to OpenAI. Privacy and compliance concerns. Need configurability around which data flows where, with audit trails.
- Hallucination is blocking enterprise adoption. Your team has tried demos. They worked sometimes. The "made stuff up" failures killed trust before adoption could spread.
- Vendor lock-in fear. Building deep on one LLM provider means you're hostage to their pricing and roadmap. Want model portability built in.
What we typically build
RAG-grounded assistants
GPT-4o or Claude grounded in your docs, contracts, KB articles, past tickets via vector embeddings. Refuses to answer outside its grounding.
In-app AI features
Smart compose, summarisation, classification, semantic search — embedded into your existing product UI, not a separate chat sidebar.
Multi-step agents
Workflows that call multiple tools (your APIs, search, database lookups) in sequence with reasoning. Action confirmation before any irreversible step.
Model-router architecture
Cheap small models (gpt-4o-mini, claude-haiku) for simple work, premium (gpt-4o, sonnet) only when needed. Cuts AI API spend 5–10× vs naive single-model.
How we deliver — 5 phases
Discovery
30-min call + 60-90 min structured scoping. Fixed-price quote.
Design
Architecture doc, you sign off before any code.
Build
Git from day 1, weekly demos, no black-box phase.
Deploy
Shadow mode → restricted live → full live.
Handover
Docs, runbook, training, 30 days bug-fix support.
Full methodology at how we build.
Pricing guidance
Single feature integration (one workflow) £2,500–£5,500. Multi-feature embed across an existing product £5,500–£12,000. Custom AI agent system £12,000–£15,000+. Run cost depends on API usage volume; we cost-optimise model routing.
Real engagement walkthroughs
No fabricated testimonials. See an illustrative end-to-end engagement:
Related practice areas
Frequently asked questions
GPT-4 vs Claude — which is better?
Different strengths. GPT-4o wins on structured output and function-calling maturity. Claude 3.7 Sonnet wins on long-context, nuanced editorial work, and instruction-following discipline. We pick per task — most projects use both. See our comparison post.
Will my data train someone else's model?
No. We configure zero-retention modes where providers support them (OpenAI, Anthropic, Mistral). Your inputs aren't retained or used for training. UK/EU hosting available.
What about open-source models (Llama, Mistral)?
Worth it for: data-residency requirements, very high volume (saving £2,000+/month in API), or specific fine-tuning. Otherwise the operational complexity rarely justifies the savings at SME scale.
How do you handle hallucination?
Five engineering layers: schema-validated output, RAG grounding, confidence routing, audit logs, reversible side effects. Hallucination becomes a logged event, not a customer-facing failure.
Can you fine-tune a model on our data?
Usually we recommend against. Modern base models + good RAG outperforms most fine-tuning at fraction of the cost. We'll be honest about when fine-tuning is the right call.
How quickly can it ship?
Single feature integration: 3–5 weeks. Multi-feature: 6–10 weeks. Larger agent systems: 10–14 weeks.
Start with a 30-minute call
Tell us about the workflow that's eating most of your team's time. We'll tell you whether gpt integrations will pay back — honestly.