LLM integration that survives production
Adding a language model to software that already has users is an engineering problem, not a prompt problem. That is the part we do.
LLM integration is the work of putting a large language model inside an application that already exists, so it behaves predictably with real data, real permissions and real traffic. It covers model selection and routing, prompt and context design, structured outputs, streaming, caching, retries, fallbacks, evaluation harnesses and cost monitoring. M Tech Solutions, a software studio founded in 2023 in Craiova, Romania, does this for founders, CTOs and product teams across the EU and North America, in English and Romanian. We usually begin with a fixed-price discovery of about a week and a vertical slice running in your stack, then continue in two-week sprints with a demo every Friday. We have shipped LLM and AI features in client products, including Scena and AutenticGift.
What we build
- Assistants and copilots inside existing dashboards, storefronts and internal tools.
- Tool-calling workflows where the model triggers real actions in your system while respecting permissions.
- Structured extraction and classification — JSON schemas, validation, deterministic fallbacks.
- Streaming interfaces on web and mobile so responses feel immediate.
- Model routing across providers so cost and latency stay under control as volume grows.
How we work
We start with the risky question: does the model actually do the job on your data? About a week in Discover, a spike and a small evaluation set answer that before anyone signs up for a roadmap.
In Shape, we decide where context comes from, what the model is allowed to do and what happens when it fails. Build runs in two-week sprints with a live demo every Friday. After Launch, we stay on call for the first month while real usage patterns emerge.
The stack we use
OpenAI and Anthropic models, LangChain when orchestration is worth the abstraction, and plain TypeScript, Node, Python or Go services when it is not. Postgres and pgvector for context storage, Redis for caching and rate limiting, Kafka for durable event flows.
Frontends in React and Next.js, mobile in Swift, Kotlin or React Native. Hosting on AWS, GCP or Vercel.
When we are a fit
- The demo works and the production version does not — wrong answers, slow answers, or a bill nobody predicted.
- You need the model wired into permissions and tenant data, not into a public sandbox.
- You want an evaluation set and a cost model before launch, not after the first invoice.
How engagements work
A discovery and a first integration slice are quoted at a fixed price. Ongoing work — new interfaces, new models, tuning — runs time and materials.
Kickoff is typically within 7–10 days, and we reply to every enquiry within one business day.
Frequently asked questions
Do you work with our existing codebase?
Yes. We usually start with a focused technical audit — architecture, code health, risks — and then integrate inside your stack rather than building a parallel service.
Which model providers do you support?
OpenAI and Anthropic are our defaults, and we build routing so a provider or model can be swapped without rewriting the product.
How do you keep token costs predictable?
Caching, context trimming, model routing by task, and monitoring of token usage and latency from day one. Cost is a design constraint for us, not a surprise at the end of the month.
How do you test something non-deterministic?
We build a small evaluation set from your real cases and run it on every meaningful change, alongside schema validation and deterministic fallbacks for the paths that must not fail.
How quickly can we see something running?
Kickoff is usually within 7–10 days, and the first sprint focuses on delivering a working vertical slice rather than a prototype.
Tell us what you are building
We reply within one business day, and most projects kick off within 7–10 days.