Engineering services · LLM applications
LLM Application Development Services
Intelligent product experiences built around real workflows.
We design and engineer generative AI features that connect securely with your product, knowledge, and operations — from focused assistants to retrieval systems and agentic workflows.
Related services: AI Chatbot Development Company in India and AI Agent Development Company.
What this service is
Who it's for, and what it fixes.
An LLM application uses a large language model as one component of real software: summarising long documents, extracting fields from contracts or forms, drafting replies, searching your knowledge in plain language, or adding an assistant to your own product. The model is the easy part. The work is in feeding it the right context, checking what it returns, keeping costs predictable, and fitting it into a workflow people trust.
We build LLM features and applications for clients as ordinary, well-engineered software with a model inside: typed inputs and outputs, retrieval from your data with permissions respected, automatic evaluation on real examples, and logs of what was asked and answered. That makes them testable, explainable and replaceable, unlike a prompt pasted into a script.
Teams drowning in documents
Contracts, reports, applications or tickets that people read just to pull out a few facts.
Product companies
An assistant, smart search or summaries to add to your own product for your customers.
Knowledge-heavy firms
Years of proposals, reports and notes that nobody can search well.
Teams with a stalled AI pilot
A prototype that looked promising but is unreliable, slow or too expensive to roll out.
Problems it solves
- Hours spent reading documents to extract the same handful of fields
- Search that only finds exact words, not what people mean
- A pilot whose answers are inconsistent and nobody can say why
- Model costs that are unknown until the bill arrives
- Worry about sensitive data being sent where it should not go
What's included
The deliverables.
What a typical engagement produces. Discovery decides which of these your first release needs.
Use-case and data review
Which task, which data, what a good answer looks like, and what must never be sent to a model, agreed before building.
Retrieval over your data
Your documents and records indexed for search by meaning, with the same access rules as the source, so people only get what they may see.
The LLM feature
Summaries, extraction, drafting or question answering built into your application or a new one, with structured, validated outputs.
Evaluation pipeline
A set of real examples and automatic scoring, run on every change of prompt, model or data, with results you can read.
Cost and speed controls
Caching, model choice per step and limits per user or task, with cost and response time tracked for every request.
Review and feedback
Ways for people to check, correct and rate outputs, feeding the next round of improvements.
How an engagement runs
From first call to running software.
Five phases, each ending in something you can see and approve. The detail, and what we need from you at each step, is on how we work with you.
- 01
Discovery 1–2 weeks
We define the task and the success measure, gather real examples, review the data and its sensitivity, and estimate running cost.
- 02
Design 1–2 weeks
How the feature fits the workflow, what context the model gets, the output format, and where a person reviews results.
- 03
Build 3–7 weeks
Retrieval, the feature and the evaluation pipeline built together; each cycle reports quality, failures, speed and cost on your examples.
- 04
Launch 1–2 weeks
Rolled out to a first group with review in place, then widened as measured quality holds on real use.
- 05
Support Ongoing
Monitoring, re-evaluation when models change, new data sources and the next features.
Technology approach
Plain, proven, and yours to keep.
Mostly the stack behind the Varynt products, chosen per project. Read how we build.
- Python
- FastAPI
- LangChain
- Spring AI
- TypeScript
- PostgreSQL
- pgvector
- Application
- Python with FastAPI or Java with Spring AI for the LLM service, and LangChain where its building blocks save time; React and TypeScript for the interface.
- Retrieval
- PostgreSQL with pgvector for search by meaning alongside keyword search, kept next to your data rather than in yet another system.
- Models
- Hosted models from the major providers, chosen per step on measured quality, speed and cost, behind one interface so they can be swapped.
- Structured output
- Model outputs validated against a schema before they reach your system, so a bad answer is caught rather than stored.
What you receive
Ownership, documentation, handover.
- The full source code in a repository you own, with its history, from the first week
- A written handover: architecture overview, how to run, build and release it, and the decisions taken and why
- Credentials, keys and accounts created in your name, never ours
- The automated test suites and the build pipeline, so anyone can keep shipping it
- The evaluation set, scoring code and the latest quality report
- A data-flow record: what is sent to which model, and what is never sent
Typical timelines
Ranges, not promises.
- Evaluated prototype
- 2–4 weeks
- One task on your real examples, with a quality score and a running-cost estimate.
- LLM feature in production
- 6–12 weeks
- Retrieval, the feature, evaluation, review and cost controls in your application.
- LLM application
- 3–5 months
- A new application around several LLM features, with its own users and roles.
Every project is quoted for its own scope after discovery; prices are not published.
Security and quality
The practices we use on our own products.
Varynt runs four products for paying customers. Client work gets the same habits. See trust and security.
- Sensitive fields masked or kept out of prompts where the task does not need them
- Outputs validated before use; nothing a model returns is executed or stored unchecked
- Every request logged with its inputs, sources, output, cost and time, for review and debugging
- Access is checked on the server for every request; hiding a button is never the control
- Code review on every change, and automated tests that run before anything is released
- Test suites run in more than one order, so a test that only passes by luck is caught
- Before a release changes a database, it is backed up and the backup is restored and checked
- Secrets live outside the code, and dependencies are kept current
What is retrieval-augmented generation (RAG)?
It means searching your own content first and giving the model the relevant passages with the question, so it answers from your information rather than from memory. It is how most business LLM applications stay accurate and current.
Do we need to fine-tune a model?
Rarely at first. Good retrieval, clear instructions and examples solve most tasks. Fine-tuning helps for very specific formats or styles at high volume, and we would only recommend it when the evaluation shows it pays off.
How do you control the cost of using LLMs?
By measuring cost per request from the first prototype, choosing smaller models for simpler steps, caching repeated work and setting limits. You see the expected running cost before rollout.
Can the application run on a model we host ourselves?
Yes, when data rules or volume justify it. Open models can run on your own infrastructure; we compare their quality on your evaluation set with hosted models before you decide.
How do you know the answers are good enough?
Each feature has an evaluation set of real examples with expected results, scored automatically on every change, plus a sample of live outputs reviewed by people.
Is our data used to train the models?
We use providers' business offerings whose terms exclude training on your data, send only what each task needs, and record the data flow so you can review it.
Related
Services and products that fit alongside.
Other services: Custom software · Mobile apps · Web applications · Workflow automation · SaaS products · Cloud and DevOps · Custom CRM
Get in touch
Tell us the task you want a model to help with.
A short call and a few real examples. You get a view of what is feasible, how it would be measured, and what it would cost to run.