AI Product Development
From AI idea to a feature your users rely on
DanixSoft is an AI product development company: it takes AI features from idea to production — scoped, tested, monitored, and with costs tracked — for startups and businesses adding AI to their products.
Most AI projects fail between the demo and production: costs spiral, answers drift, and nobody can tell whether it is working. We build AI features the same way we build the rest of the product — scoped, tested, monitored, and owned by engineers who also understand the surrounding system.
What's included?
Everything you need to ship a production-grade solution.
AI Opportunity Assessment
We look at your workflows and data and identify where AI will actually save time or unlock revenue — and where it will not.
LLM Features in Your Product
Chat, summarisation, drafting, and classification features built into your existing web or mobile app.
RAG & Document Intelligence
Search and question-answering over your documents, contracts, tickets, or knowledge base, with citations.
AI Agents & Automation
Multi-step workflows that research, enrich, route, and update records across your tools — like the queue workers and scheduled pipelines behind our own products.
AI Copilots for Internal Teams
Assistants for support, sales, and operations teams that draft, look up, and hand off to humans.
Evaluation, Cost & Safety
Test sets, prompt versioning, per-feature cost tracking, rate limits, and output guardrails.
How does a project run?
Transparent, milestone-driven delivery from day one.
Use-Case Validation
Agree the problem, the data available, and how success will be measured before building anything.
Two-Week Prototype
A working prototype on real data so you can judge quality before committing to a full build.
Product Integration
Integrate the AI feature into your product with proper UX for loading, errors, and human review.
Production Hardening
Evaluation suites, cost limits, logging, fallbacks, and security review of prompts and tool access.
Monitor & Improve
Track quality and cost in production and iterate on prompts, retrieval, and models over time.
Which technologies do we use?
Battle-tested tools we rely on to deliver.
AI Product Development 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 AI feature in an existing product usually fits the MVP tier. AI-first products and multi-agent systems typically sit in Product / Growth or above.
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 kind of AI work do you do?
Mostly applied AI inside real products: LLM features, retrieval over company data, AI agents that automate multi-step workflows, and automation pipelines — for example Framewise, our AI website builder, which turns a one-line description into a structured site plan with a single model call.
Can you add AI to our existing application?
Yes. Most of our AI work is added to existing web or mobile products. We integrate through your backend so keys, costs, and data access stay under control.
How do you keep AI costs under control?
Per-user and per-feature limits, caching, choosing the smallest model that meets the quality bar, and tracking token spend per feature from day one.
How do you handle wrong answers?
We design for them: retrieval with citations, validation of structured outputs, human review for high-stakes actions, and evaluation sets that catch regressions before release.
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.