AI features that hold up with real users.
Flazex designs and builds production AI features and agents, such as assistants, search over your own data and tool-using workflows, inside new or existing products.
First working AI feature usually demoed within 1-2 weekly sprints
Things we could build for you.
Support assistant on your docs
An in-app assistant that answers product questions from your help center and hands off to a human when unsure.
Document intake and extraction
Upload invoices, contracts or forms and get clean structured fields back, ready for review and import.
Internal ops agent
An agent that reads incoming requests, looks up records in your tools and drafts the next action for your team.
What usually goes wrong, and what we do instead.
- 01
Your AI prototype works in a demo but gives wrong or made-up answers with real data.
We ground answers in your own content with retrieval and test them against an eval set before release.
- 02
You are not sure which AI feature is worth building first.
We start from the tasks your users repeat most and pick the one where a model saves clear time.
- 03
Token spend and latency climb as usage grows, and one provider outage takes the feature down.
We add caching, model routing, cost controls and fallbacks so the feature stays fast and available.
What you get.
- 01
Use case and model selection
A short written plan naming the feature, the model per task and how we will measure if it works.
- 02
AI features in your product
Chat assistants, summaries, extraction and generation wired into your UI with streaming responses.
- 03
RAG over your data
Ingestion, chunking, embeddings and retrieval so answers cite your docs, tickets or records.
- 04
Agents with tools
Agents that call your APIs, look things up and take actions, with clear limits and human approval where it matters.
- 05
Evals and monitoring
Test sets, quality checks and logging so you can see when answers drift and fix them before users notice.
- 06
Docs and handover
Prompts, pipelines and dashboards documented and kept in your repo and accounts from day one.
We've shipped these in public.
Open source work by Sudhan, who designs and builds every Flazex project.
How we'll build it.
Step 1
Pick the job
On a free 30-min call we find the task worth automating and agree what a good answer looks like.
Step 2
Build an eval set
We collect real examples from your data so every prompt and model change is tested, not guessed.
Step 3
Ship the feature
We build the feature in weekly sprints and demo it working on your data each week.
Step 4
Harden for production
Monitoring, cost controls and fallbacks go in before rollout, then we hand over docs and dashboards.
Built with
OpenAI/Anthropic/Open models/Vercel AI SDK/TypeScript/Next.js/Postgres with pgvector/Vercel
Questions about ai features and agents
Yes. Most AI work goes into products that already exist. We integrate with your current stack, data and auth instead of starting over.
Ready to ship something great?
30 minutes today. A clear plan tomorrow. A shipped product in weeks. Only 2 build slots open for November.
Free 30-min call. No pitch deck, no obligation. Typical reply in 12 hours.