AI & LLM Integration Services
We wire language models into products that already have users, budgets, and uptime expectations — retrieval over your own data, conversational interfaces, and voice.
What is LLM integration?
LLM integration is the work of connecting a large language model to a product's own data, interface, and permissions so it produces useful, grounded answers rather than generic text. In practice it involves retrieval-augmented generation (RAG), prompt and context design, evaluation, and cost control. Techroniqs has shipped this with OpenAI, Claude, Mistral, and Speechmatics — including EliasHealth, a UK healthcare platform combining voice recognition with conversational AI for clinicians, and AI-powered product recommendations inside the Edi-Mart marketplace.
Key takeaways
- Most AI features do not need a fine-tuned model. Retrieval over your own documents solves the majority of business cases at a fraction of the cost.
- RAG grounds a model in your data by retrieving relevant passages and passing them to the model with the question, which is what reduces hallucination.
- The hard part is rarely the model call. It is the retrieval quality, the evaluation set, and the cost per request at scale.
- Model choice should be a configuration value, not an architectural commitment — we build so the provider can be swapped.
- An AI feature without an evaluation set cannot be improved, only guessed at.
Who this is for
- Product teams that want an AI feature grounded in their own documents, tickets, or catalogue.
- Companies with a support or operations bottleneck that a well-scoped assistant could absorb.
- Teams that have a working prototype in a notebook and need it turned into something that survives production traffic.
What you get
Every engagement produces these. Anything else is scoped explicitly rather than assumed.
- Retrieval pipeline
- Ingestion, chunking, embedding, and the retrieval layer over your own content.
- Model integration
- Provider-agnostic layer so OpenAI, Claude, or Mistral can be swapped without a rewrite.
- Evaluation set
- A fixed set of questions and expected behaviour, so changes can be measured rather than eyeballed.
- Cost and rate controls
- Caching, token budgets, and per-user limits before launch, not after the first invoice.
- Product interface
- The chat, search, or assist surface your users actually touch, built into your existing app.
- Guardrails
- Scope limits, refusal behaviour, and logging for what the model was asked and what it returned.
Technology stack
Models
- OpenAI
- Claude
- Mistral
Speech
- Speechmatics
Application
- Next.js
- React
- tRPC
- Node.js
- FastAPI
- Flask
Data
- PostgreSQL
- Supabase
- Redis
RAG, fine-tuning, or prompt engineering
These three are often presented as competing options. They solve different problems, and picking the wrong one is the most common and most expensive mistake in an AI project.
| Approach | Solves | Cost to build | Cost to change |
|---|---|---|---|
| Prompt engineering | Tone, format, and simple task shaping | Lowest | Trivial — edit text |
| RAG (retrieval) | Answering from your own private or changing data | Moderate | Low — reindex content |
| Fine-tuning | Consistent behaviour or format the base model will not follow | High | High — retrain and revalidate |
| Tool and function calling | Taking actions in your systems, not just answering | Moderate | Low — add or edit tools |
Our default recommendation is prompt design plus retrieval. Fine-tuning is worth it when you have evidence the first two are insufficient.
How we build it
- Build the evaluation set before the feature. Twenty real questions with known-good answers will tell you more than any demo.
- Ground answers in retrieved content and show the source, so users can verify and trust what they read.
- Cache aggressively. Identical questions are far more common than teams expect, and caching cuts both latency and spend.
- Keep the model provider behind an interface. Prices, limits, and capabilities move quickly.
- Log prompts and responses from day one, with the same care you apply to any other user data.
- Set a hard per-user rate limit before launch. An unbounded AI endpoint is a billing incident waiting to happen.
Common mistakes
Reaching for fine-tuning first
Weeks of work and recurring training cost for something retrieval would have solved.
Instead: Try prompt design and retrieval, and only fine-tune with evidence they fall short.
No evaluation set
Every change is a guess, and quality regressions ship unnoticed.
Instead: Fix a question set early and re-run it on every change.
Dumping whole documents into the prompt
Cost per request climbs and answer quality drops as the relevant passage gets buried.
Instead: Chunk deliberately and retrieve only what is relevant.
Shipping without cost controls
A single scripted user can generate an unbounded bill.
Instead: Rate-limit per user and cap tokens per request before launch.
Presenting model output as authoritative
Users trust a wrong answer in a domain where being wrong matters.
Instead: Cite sources, and scope the assistant to what it can actually answer.
Typical timeline
Ranges reflect engagements we have delivered. Scope, integration count, and decision speed on your side move these more than anything else.
1. Scoping and evaluation set
1–2 weeksA defined use case and a fixed question set to measure against.
2. Retrieval prototype
2–3 weeksGrounded answers over your real content, measured on the evaluation set.
3. Product integration
3–8 weeksThe feature inside your application with guardrails and cost controls.
4. Tuning and operation
OngoingRetrieval quality, latency, and cost per request tracked and improved.
Where we have done this
EliasHealth
HealthTechAI-powered health platform with voice recognition and conversational AI for healthcare professionals.
Read the case studyCresta.ai
AI / LLMConversation Intelligence
Enterprise SaaS applying generative AI and conversation intelligence to contact centers — providing real-time guidance for customer-facing teams.
99%+ uptime on enterprise AI UI
Read the case studyEdi-Mart
E-CommerceSmart-EDi
B2B and B2C re-use marketplace platform with AI-powered product recommendations, location-based services, and integrated payments.
Read the case studyVibeSEO
AI / LLMAI-powered SEO automation tool for JavaScript-heavy web apps. Performs automated audits and generates AI-powered SEO reports.
Read the case studyCropTalk
AgriTechAI-powered communication platform for farmers to connect, share knowledge, and access expert guidance.
Read the case studyWhen this is the wrong choice
Stated plainly, because finding this out in month three costs you far more than reading it now.
- A language model is a poor fit where every answer must be provably correct and auditable — arithmetic, compliance determinations, and medical or legal advice belong behind deterministic logic or a qualified human.
- Retrieval cannot fix content that does not exist. If the answer is not written down anywhere, no amount of engineering will retrieve it.
- We do not train foundation models. We integrate existing ones.
- Per-request costs are real and recurring. An AI feature changes your unit economics, and we will model that with you before building.
What to consider instead
- Conventional search
- Users know what they are looking for and need to find a document, not an answer.
- Rules or decision tables
- The logic is finite, known, and must be auditable.
- Off-the-shelf AI SaaS
- Your use case is generic — a standard support bot on public content.
Frequently asked questions
What is retrieval-augmented generation (RAG)?
RAG is a technique where the system first retrieves relevant passages from your own content, then passes those passages to a language model along with the user question. The model answers from the supplied material rather than from memory alone, which is what makes answers current, specific to your business, and traceable to a source.
Do we need to fine-tune a model?
Usually not. Fine-tuning changes how a model behaves, not what it knows, so it is the wrong tool for answering from your data — that is what retrieval does. Fine-tuning earns its cost when you need a consistent output format or behaviour the base model will not reliably follow, and you have evaluation evidence that prompting and retrieval fall short.
Which model provider do you use?
We have shipped with OpenAI, Claude, and Mistral, and Speechmatics for speech recognition. We keep the provider behind an interface so it can be changed as pricing and capability move, rather than hard-wiring one vendor into the architecture.
How do you stop the model making things up?
Three things in combination: ground every answer in retrieved content rather than model memory, show the source so users can verify, and scope the assistant so it declines questions outside what it can support. No technique removes the risk entirely, which is why we do not recommend LLMs where every answer must be provably correct.
What does an AI feature cost to run?
Cost is per request and recurring, driven by how much text goes into and out of the model. That changes your unit economics, so we model expected cost per user before building and put caching, token limits, and per-user rate limits in place before launch.
How long does an AI integration take?
A grounded retrieval prototype measured against a real question set takes about three to five weeks including scoping. Integrating it into a production product with guardrails and cost controls typically adds three to eight weeks depending on how much of the surrounding application already exists.
Can you add AI to our existing product?
That is the majority of what we do. Cresta.ai, EliasHealth, and Edi-Mart all involved building AI capability into an application rather than starting from an empty repository.
Related
Talk to an engineer about ai & llm integration
A 30-minute call with someone who will be writing the code. We will tell you if we are the wrong fit, and what we would do instead.