AI and LLM App Development
We add LLM-powered features to real products: search and question answering over your documents, assistants that take actions in your systems, and automation of repetitive text-heavy work.
A demo is easy to build and a reliable feature is not. We set up evaluation on your own data early, so that every change to a prompt or a model is measured before it reaches users.
Question answering over your documents, tickets and databases, with citations back to the source.
Assistants that call your APIs to look up records, draft replies, file tickets and complete multi-step tasks.
Extraction, classification and summarization pipelines for contracts, invoices, emails and support logs.
Test sets built from your data, quality tracking, prompt caching and model routing to keep spend predictable.
Step 1
You send us a description of the problem, the users and the constraints. We reply in writing with questions within one working day.
Step 2
Within a few working days you receive a scope, a timeline and a price broken down by feature.
Step 3
We build in one-week cycles. Each ends with a recorded demo and a version you can open and test yourself.
Step 4
We deploy to your accounts, document the system and either stay on for maintenance or hand over to your team.
In-house product, in development
An in-house product in development: a Flutter media player for six platforms that generates subtitles with AI and keeps long-running jobs alive across screens and restarts.
Usually not. Most products get better results from retrieval over their own data combined with a well-designed prompt. We consider fine-tuning only when evaluation shows that retrieval and prompting have reached their limit.
We use API providers whose commercial terms state that API data is not used for training by default, and we confirm the current terms with you before the project starts. For stricter requirements we can deploy through your own cloud account.
We ground answers in retrieved sources, show citations, instruct the model to say when it does not know, and measure accuracy on a test set built from your data. No approach removes errors entirely, so we also design the interface to make answers easy to verify.
We estimate it from your expected volume before building, then reduce it with caching, smaller models for simple steps and batching where latency allows. You get a dashboard showing spend per feature.
Yes. Most of our AI work is adding features to an existing web or mobile product through its current backend and authentication.
September 29, 2026
Fixed price and hourly contracts move risk in different directions. Here is how each one works, where each fits, and a middle option that suits most projects.
September 29, 2026
A plain-language guide to choosing between retrieval-augmented generation and fine-tuning for an LLM feature, with a decision process you can follow.
September 29, 2026
A one-page software project brief gets you accurate quotes and fewer surprises. Here is what to include, what to leave out, and a template you can copy.
Service
SaaS products, customer portals, dashboards and internal tools built with React, Next.js and Node.js.
Service
Cross-platform and native apps for iOS and Android, including the backend and the store submission.
Tell us what you are building. We reply within one working day with next steps.