RAG vs. Fine-Tuning: Key Differences, Benefits, and Use Cases
RAG or fine-tuning for your AI app? Learn the key differences in accuracy, data updates, cost and performance, when to use each, and how to combine them.
AI & Automation21 Oct 2026
AI Development
AI features that survive contact with real users — measured accuracy, capped costs, and a path to production.
Services03 / 06
AI Development
AI features that survive contact with real users — measured accuracy, capped costs, and a path to production.
- Deliverables
- 05
- artefacts handed over
- Phases
- 04
- build milestones
- Engagement
- AI
- AI Development
AI Development
Most AI projects stall between the demo thatimpressed the board and the version a customer canbe trusted with. We build the second one: retrievalthat cites its sources, evaluations that run in CI,cost ceilings you set, and a human fallback forevery path the model can get wrong.
The problem
Sound familiar?
If more than one of these describes where you are today, this is the service that fixes it.
The prototype works, production doesn't
A notebook demo has no rate limits, no evaluation suite, and no answer for the request that costs $40 in tokens.
Nobody can say whether it's actually right
Without a labelled evaluation set, 'it seems better' is the only quality signal — and it isn't one you can take to a customer.
Locked to one model provider
Prompts and SDK calls scattered through the codebase turn a pricing change or a better model into a rewrite.
Sensitive data going somewhere unclear
Security review stops most AI pilots, and it stops them late, after the build is already paid for.
How we deliver it
How we deliver it
- 1
Retrieval that cites its sources
RAG over your own content with chunking and reranking tuned to your documents, and every answer traceable to the passage it came from.
- 2
Evaluations before features
We build a labelled evaluation set first, then run it in CI on every change, so accuracy is a number that moves rather than an opinion.
- 3
Model-agnostic by design
One interface in front of Claude, GPT and Gemini. Switching provider or model is a config change, not an engineering project.
- 4
Cost and rate controls
Per-tenant token budgets, caching, and smaller models routed to the requests that don't need a frontier one.
- 5
Agents and workflow automation
Tool-using agents for multi-step internal work, with durable execution so a failed step resumes instead of restarting.
- 6
Security review built in
Prompt-injection handling, PII redaction, data residency and a subprocessor list ready before your security team asks.
Deliverables
What you receive
- Working AI feature deployed to your production environment
- Labelled evaluation suite wired into CI
- Cost and latency dashboard with per-feature breakdown
- Architecture decision records covering model, retrieval, and fallback choices
- Security and data-handling documentation for review
Timeline
A typical schedule
- 1
Discovery & evaluation design
1-2 weeks
- 2
Retrieval & prototype
2-3 weeks
- 3
Hardening, evals & cost controls
2-4 weeks
- 4
Production rollout & handover
1-2 weeks
Insights
Related reading
FAQs
Frequently asked questions
Other things we build
All servicesWeb AppsSaaS platforms, dashboards and internal tools built to carry real load and real customers.Mobile AppsiOS and Android apps, designed and built together — shipped to both stores and rated well enough to keep.DevOps & CloudDeploy on a Friday. Know within sixty seconds when something breaks.Software DevelopmentCustom platforms, SaaS products, MVPs and AI features — architected once, built properly, and handed over running.QAManual, automated, performance, API and mobile testing — coverage at every layer, so releases ship with confidence.
- LLM apps in production
- RAG over your own data
- AI agents that finish tasks
- Claude, GPT and Gemini
- Evals before every release
- No vendor lock-in