Generative AI Solutions
LLM features built for production
DanixSoft builds generative AI and LLM features — drafting, summarising, search, and multi-step automation — into web and mobile products, with evaluation, guardrails, and cost tracking from the first sprint.
Large language models make it possible to add drafting, summarising, searching, and multi-step automation to almost any product. The hard part is making them reliable and affordable. We build generative AI features with evaluation, guardrails, and cost tracking from the first sprint.
What's included?
Everything you need to ship a production-grade solution.
LLM Integration
OpenAI, Anthropic Claude, and Gemini integrated behind your backend with streaming, retries, and fallbacks.
RAG over Your Data
Chunking, embeddings, vector search, and re-ranking so answers come from your documents, with citations.
AI Agents & Tool Use
Agents that call your APIs to research, enrich, and update records — with human approval where it matters.
Content Generation
Generating product copy, posts, and documents at scale — the same structured-output approach we use in Framewise, our AI website builder.
Structured Extraction
Turning emails, PDFs, and forms into validated, structured data.
Evaluation & Guardrails
Test sets, prompt versioning, output validation, and per-feature cost and rate limits.
How does a project run?
Transparent, milestone-driven delivery from day one.
Define the Job
Agree exactly what the model should do, for whom, and how we will know it is good enough.
Prototype
A working prototype on your real data within about two weeks.
Evaluate
Build a test set and measure quality before and after every prompt or model change.
Harden & Integrate
Guardrails, cost limits, logging, and UX for loading states, errors, and human review.
Operate
Monitor quality and spend in production, and iterate as models improve.
Which technologies do we use?
Battle-tested tools we rely on to deliver.
Generative AI & LLM Integration in practice
Case studies where this service was part of the build.
FramewiseAI website builder: describe a business, get a finished site
Challenge: Small businesses need a professional website without hiring a developer, but most builders hand them a template marketplace and leave the structure, copy, and design decisions to them.
Read the case studyLinkGenAI-written LinkedIn posts in your voice, scheduled and published for you
Challenge: Professionals know they should post on LinkedIn regularly, but writing in their own voice every week, finding an image and remembering to publish at a good time is the part that never gets done.
Read the case study
How much does it cost?
MVP / Launch: from ~$3k
Typically 4–8 weeks
A single generative AI feature usually fits the MVP tier; AI-first products and agent systems are typically Product / Growth.
Typical starting points, not quotes. Final pricing depends on scope and is fixed in writing after a discovery call.
See all pricing tiersFrequently Asked Questions
Common questions about this service.
What is retrieval-augmented generation (RAG)?
A pattern where the system first retrieves relevant passages from your own documents, then asks the model to answer using only those passages. It reduces made-up answers and lets you cite sources.
Which model should we use?
It depends on quality, latency, cost, and data-handling requirements. We usually prototype with more than one and pick the smallest model that meets the quality bar.
Can the AI take actions in our systems?
Yes, through tool calling against your APIs. For anything irreversible or high-stakes we add human approval steps and full audit logs.
Where to Next
Services teams usually pair with this one.
Further reading: AI & ML articles on the DanixSoft blog
Industries we build this for
Next step
Tell us what you are building.
A free 30-minute discovery call, then a written scope and a fixed-price quote — before you commit to anything.