LLM Integration
Put a large language model to work inside your product
We embed LLMs into your features and internal workflows the right way — grounded in your data, measured for quality, guarded against misuse, and kept on budget.
Talk to usFrom a clever demo to a reliable production feature
Wiring an API call to a large language model takes an afternoon. Shipping an LLM feature that is accurate, safe, fast, and affordable at scale takes engineering. The gap between a prompt that works in a notebook and a feature thousands of users rely on is where most AI projects stall: outputs drift, costs balloon, prompts leak, and nobody can prove the thing actually got better between releases. The demo dazzles the room and then quietly dies on the way to production, because a demo only has to work once and a feature has to work every time.
What makes that last mile hard is that LLMs are probabilistic, not deterministic. The same prompt can return a great answer today and a wrong one tomorrow, and a change that fixes one case can silently break ten others. Without a way to measure quality, every release is a guess; without guardrails, a single crafted message can make your feature say something it never should; and without cost control, a successful launch can hand you a token bill that erases the business case. These are engineering problems, and they have engineering answers.
Techies closes that gap. We integrate models such as Claude and other leading LLMs into your products and back-office workflows, grounding them in your own data with retrieval-augmented generation so answers stay accurate. We build evaluation harnesses that score quality on every change, add guardrails that block prompt injection and unsafe output, and engineer the caching, routing, and model selection that keep your token bill predictable. You get a feature you can trust in production — and one accountable team owning it end to end, from the retrieval layer to the monitoring dashboard.
Grounding, evaluation, and the discipline that makes LLMs reliable
Accuracy starts with grounding. A raw model answers from its training data, which is generic, frozen at a point in time, and blind to your business. We connect the model to your knowledge through retrieval-augmented generation: documents are chunked, embedded, and stored in a vector index, and at query time the most relevant passages are pulled in so the model reasons over your real, current data instead of its memory. Done well, this is what turns a plausible-sounding answer into a correct one — and lets the feature cite its sources so users can trust it.
Reliability comes from measurement. The single biggest difference between an AI feature that improves and one that decays is whether the team can tell, objectively, if a change made things better or worse. We build evaluation harnesses — curated test sets, automated scoring for accuracy, tone, and safety, and regression checks that run on every prompt or model change. With evals in place you ship improvements with evidence instead of vibes, catch regressions before users do, and can confidently swap to a cheaper or newer model the moment it passes your bar.
Safety and cost are the constraints that decide whether the feature survives contact with real users. We add input and output guardrails that defend against prompt injection, block unsafe or off-brand responses, and keep the model inside its intended job. In parallel we engineer the economics: caching repeated work, routing each request to the smallest model that passes its evals, trimming context to what matters, and dashboarding spend so cost never surprises you. Accurate, safe, and affordable are not trade-offs you pick one of — they are requirements you design for together.
What LLM integration covers
RAG & data grounding
We connect the model to your knowledge through retrieval pipelines, embeddings, and vector search, so responses cite your real data instead of hallucinating. We handle chunking, indexing, and keeping the index fresh as your content changes, which is where most naive RAG implementations quietly fall apart. The payoff is answers that are accurate, current, and traceable back to a source.
Evaluation & quality gates
Automated eval suites score accuracy, tone, and safety on every prompt or model change, so you ship improvements with evidence, not vibes. We build the test sets from your real use cases and wire the evals into CI, so a regression fails the build instead of reaching production. This is what lets you iterate quickly without breaking what already works.
Guardrails & safety
Input and output filtering, prompt-injection defenses, and policy checks keep the model on-topic, on-brand, and out of trouble. We treat every user input as untrusted, constrain what the model is allowed to do and say, and block attempts to manipulate it into unsafe behaviour. The result is a feature you can put in front of the public without holding your breath.
Cost & latency control
Caching, smart model routing, and right-sized context windows cut token spend and tail latency without sacrificing answer quality. We route easy requests to small, cheap models and reserve the expensive ones for cases that truly need them, then cache repeated work so you never pay twice. The result is an LLM feature whose unit economics actually work at scale.
Observability & monitoring
Tracing, logging, and dashboards on quality, cost, and failures give you a live view of how the model behaves with real users. When something goes wrong you can see the exact prompt, retrieval, and response that caused it, instead of guessing in the dark. Continuous monitoring turns the inevitable surprises of production into fixable, understood events.
Agentic workflows & tool use
When a single answer is not enough, we build the model into multi-step workflows that call your tools, query your systems, and take real actions under controlled conditions. We define exactly which tools the model can use, validate every call, and keep a human in the loop where the stakes demand it. This is how an LLM moves from answering questions to actually getting work done.
Prompt & context engineering
Getting reliable behaviour out of a model is a craft of instructions, examples, and carefully assembled context, not a single clever sentence. We design and version your prompts, structure the context the model sees, and tune them against your evals so quality is reproducible rather than accidental. When the model misbehaves, we have a disciplined way to diagnose and fix it.
Model selection & migration
The best model for your task changes as the field moves, and being locked to one provider is a risk. We benchmark candidates against your real workload, design the integration so swapping models is a configuration change rather than a rewrite, and re-evaluate as new models ship. You stay on the best price-to-quality option without re-engineering every time the landscape shifts.
Frequently asked questions
- Which LLM should we use?
- It depends on your task, latency, and budget — there is no single best model for everything. We benchmark candidates such as Claude and other leading models against your real workload, then route each request to the most cost-effective model that meets your quality bar. We also design the integration so changing model later is a configuration change, not a rewrite, because the landscape moves fast and you should never be locked in. The right answer is usually a mix, matched to each task.
- How do you stop the model from hallucinating?
- We ground it in your data with RAG so it answers from your real content rather than its memory, constrain it with clear instructions, and run evaluation suites that catch regressions before release. When the model is unsure or the knowledge base has no match, we configure it to defer rather than guess. No technique makes hallucination literally impossible, but grounding plus evaluation plus the freedom to say "I don't know" reduces it to a level you can actually ship on.
- How do you keep token costs under control?
- We cache repeated work so you never pay twice for the same answer, trim and route context efficiently so you are not sending more tokens than the task needs, and pick the smallest model that passes your evals for each request. We also dashboard spend in real time, so cost is something you watch and manage rather than discover on an invoice. The goal is unit economics that still work when the feature is successful, not just when traffic is small.
- Can you work with our existing codebase?
- Yes. We integrate into your stack through clean APIs and SDKs, fit your deployment and security model rather than imposing ours, and leave you with documented, tested code your own team can maintain. We are comfortable extending an existing application instead of demanding a rebuild, and we align with your conventions and ecosystem. Everything ships in your repositories so you are never dependent on us to keep it running.
- How long does an LLM integration take?
- A focused first feature — grounded, evaluated, and guarded — typically reaches production in a few weeks, and we aim to show you working software early rather than after a long silence. The timeline depends mostly on the state of your data, the complexity of the workflow, and how many systems the model needs to touch. We start with a contained scope, prove it with evals on real inputs, then expand from a foundation that already works.
- Is our data safe if we use a third-party model?
- We design the integration around your privacy requirements, choosing models and deployment options that fit your data residency and confidentiality needs, and we keep sensitive data out of logs and out of any flow it does not belong in. We work with providers whose terms do not train on your data, and where regulations demand it we can architect for stricter isolation. Data protection is a design input from the start, not a concern we address after the fact.
- What if a newer, better model comes out later?
- That is exactly why we build the integration to be model-agnostic. Swapping to a newer or cheaper model becomes a configuration change validated against your existing evals, rather than a re-engineering project, so you can adopt improvements as the field moves. We re-benchmark periodically and flag when a change would lower your cost or raise your quality. You ride the curve of a fast-moving field instead of being stranded on whatever you picked first.
- Do we need our own AI team to maintain this?
- No. We deliver documented, tested code along with the evals, dashboards, and runbooks your existing engineers need to operate it confidently. The evaluation harness in particular means your team can change prompts or models and immediately see whether quality held, without deep ML expertise. You can run it yourself, keep us on for ongoing iteration, or hand it to another partner — the build is structured so the choice is genuinely yours.
- How is this different from just calling an API ourselves?
- Calling the API is the easy 10 percent; the hard 90 percent is everything that makes the feature reliable in production — grounding, evaluation, guardrails, cost control, observability, and the discipline to keep all of it healthy as the product evolves. Teams that skip that work usually ship something impressive that degrades, gets expensive, or causes an incident within months. We build the engineering around the API call that turns a demo into a feature you can depend on.
Ready to ship an LLM feature you can trust?
Tell us what you want the model to do and where it lives in your product, and we'll come back with an integration plan covering RAG, evaluation, guardrails, and cost.
Get started