Should we fine-tune a model or use RAG?

Usually RAG first — it's cheaper, faster to iterate and easier to debug. We fine-tune when RAG can't close the accuracy gap, the domain is narrow, or latency/cost demands a smaller model. We'll recommend in the audit.

Which LLM do you use?

Whichever fits the use case — GPT-4/4o, Claude, Gemini, or open-source Llama/Mistral for on-prem or cost-sensitive workloads. We wire multiple models behind one interface so you can switch without rewriting.

How do we know the model is accurate?

We build a labelled eval set for your use case and run every change against it. You see accuracy numbers, not 'it feels better'. We also track production metrics — answer rate, escalation rate, human-edit rate.

What about data privacy?

We can deploy on VPC, on-prem or use API providers with zero-retention contracts. Your data is never used to train third-party models. We'll document the data flow before any code is written.

How much does it cost to build a custom LLM app?

A production RAG assistant typically lands £25k-£80k for the build, plus £1k-£5k monthly for inference and eval infrastructure. Fine-tuning a custom model adds £10k-£30k depending on data volume and labelling needs.

How long does it take to build an LLM-powered feature?

A scoped RAG prototype over your documents ships in 2-4 weeks. Production-grade with evals, guardrails, observability and CI typically takes 8-12 weeks. We ship the prototype first, then harden it — never the other way around.

Should I fine-tune a model or use RAG?

RAG first — fine-tuning is for style, tone and format, not for adding new knowledge. If RAG over your documents doesn't hit accuracy targets, fine-tune on labelled examples of the desired output style. We measure both before recommending.

Can you deploy LLMs on our own infrastructure?

Yes — VPC, on-prem or air-gapped. For regulated workloads we host open-source models (Llama 3, Mistral) on your GPU instances with zero-retention contracts on any API provider. Your data never trains a third-party model — documented before any code ships.

AI & Automation

Custom LLM Solutions

We build LLM systems that ship to production — RAG over your real data, fine-tuning where it earns its keep, eval harnesses for regressions, guardrails for safe outputs, and cost controls. You get measured accuracy, a fallback chain and a runbook for misbehaviour.

📊 At a glance

Models: OpenAI · Anthropic · Llama · Mistral
Retrieval: RAG over 18M+ docs
Hosting: Private VPC · on-prem option
Eval suite: Golden + adversarial sets

What is Custom LLM Solutions?

What is a custom LLM solution? A custom LLM solution is the engineering of language-model features including retrieval-augmented generation, fine-tuning, agentic tools and eval suites, tailored to a specific business workflow. ClickTake delivers custom LLM solutions for UK enterprises, using GPT-4, Claude, Gemini and open-source models behind one typed interface with production RAG.

What You Get

Built around outcomes.

Every engagement ships with these deliverables baked in — not bolted on later.

✅

RAG over your real data

Retrieval over your actual documents, tickets and knowledge base — not a generic chatbot trained on the open web.

⚡

Fine-tuning when warranted

We fine-tune only when prompts and RAG can't close the gap — and we measure lift against the baseline before you pay for it.

🚀

Eval harness included

Every prompt and model change runs against a labelled eval set — so accuracy is measured, not vibes-based.

🎯

Guardrails for safety

Input and output moderation, PII redaction, jailbreak detection and topic filtering — shipped, not 'we'll add it later'.

🛡️

Cost control by default

Token budgeting, prompt caching, model routing and small-model fallbacks — so your LLM bill scales with revenue, not surprise.

📈

Multi-model & fallback

OpenAI, Anthropic, open-source (Llama, Mistral) wired behind one interface — so a model outage doesn't take your product down.

Tech stack

The production stack we ship for custom llm solutions.

RAG pipelinesFine-tuningEval harnessGuardrails

Senior engineers (8+ yrs avg) own every engagement. CI/CD from day one, observability baked in, and a p99 120ms performance budget enforced in CI.

How we deliver it

A tailored 4-step process for custom llm solutions.

Use case & data audit

We map the use case, available data, accuracy targets and cost ceiling — and decide whether RAG, fine-tuning or both are warranted.

RAG pipeline & eval set

We build the retrieval pipeline (embeddings, vector store, reranker) and a labelled eval set — so every change is measured.

Guardrails & cost controls

We add input/output moderation, PII redaction, token budgeting and model routing — safety and cost designed in, not bolted on.

Production & monitoring

We ship to production with logging, eval-on-deploy and a runbook for the inevitable 'the model is misbehaving' page.

FAQ