◆ Services · AI
AI Product Development. AI features that justify the cost.
AI product development for founders and small businesses. We build AI features and AI-first SaaS with Claude, GPT, RAG, and embeddings, focused on the ones users actually pay for.
- Models
- Claude, GPT
- Stack
- RAG, embeddings
- Timeline
- 8 to 16 weeks
- Status
- Open
◆ The short answer
AI product development is the work of designing and shipping AI features that earn revenue, not just demos. We build with Claude, GPT, retrieval-augmented generation, and embeddings, on the same Next.js and Postgres stack as our SaaS work. Typical AI products take eight to sixteen weeks and start at mid five figures.
◆ TL;DR
- AI features that move a metric, not party tricks.
- Claude or GPT depending on the task. Often both.
- RAG, embeddings, and tool use built into the product, not bolted on.
- Cost-aware. We track tokens, latency, and quality every week.
Last updated
◆ How we build it
Three phases. Three demos. One live product.
Discover
A short discovery phase. We frame the riskiest assumption first, sketch the user, and agree on what we are actually shipping. By the end you have a written brief, a clickable prototype, and a fixed scope you can sign off.
Build
Iterative build cycles with weekly demos. Engineering joins at the design table from day one, so design and code move together. Feature flags on anything risky. Production observability built in from the first commit.
Launch and scale
Soft launch to a closed beta. Telemetry on every flow. Then a coordinated public launch and the unglamorous scaling work: auth, queue depth, database read replicas. We stay in the trenches after launch, not before it.
◆ What you get
Concrete deliverables.
Every engagement produces artifacts you can hand off, build on, or hold us accountable to. No abstract decks.
- A live AI product on a domain you own
- Claude or GPT integration with cost tracking
- RAG over your content with citations and freshness
- Evals and quality gates, not just vibes
- Privacy and data handling reviewed and documented
- A handoff doc, a runbook, and an optional retainer
◆ Stack
How we build it.
Claude as the default for chat, agents, and long-context work. GPT for structured output and image. Postgres pgvector for retrieval. Custom evals over off-the-shelf benchmarks. Trace tooling so you can debug a single conversation.
- Anthropic Claude
- OpenAI GPT
- Vercel AI SDK
- Postgres pgvector
- Embeddings
- LangChain
- Trace tooling
◆ Why Thinkmakr
Notesmakr ships a production RAG pipeline today: ingestion, chunking, embedding, retrieval, citation, and Q-and-A against user notes. We bring that pattern to your build instead of starting from scratch.
◆ How we work
- No offshoring. The team that scopes the work ships the work.
- Your code, your repo, from day one. Full ownership at handoff.
- Fixed milestones and written proposals. No surprise invoices.
- Reviewable PRs. You can read every line before it ships.
- We tell you if we are the wrong fit and recommend who is.
◆ Receipts
Live products that prove the studio works.
The strongest argument we can make is the software we already ship.
◆ Common questions
Things people ask before they brief us.
How much does AI product development cost?
A Thinkmakr AI product typically ranges from mid five figures for a focused AI feature to low six figures for an AI-first SaaS. Token costs and quality work add ongoing spend; we track and report both.
How long does AI product development take?
Eight to sixteen weeks from signed proposal to a live product. Evals, quality gates, and prompt tuning add three to four weeks compared to a non-AI product.
Should I integrate ChatGPT into my mobile app?
Sometimes. The right question is what user problem the AI solves. If it speeds up an action that already exists in your app, yes. If it is a chat box bolted to a homepage, usually not.
Are my data and prompts private?
Yes. We default to API providers with no-training contracts (Anthropic and OpenAI both offer this). We document data retention and pass-through clearly in the proposal.
How do you measure AI quality?
Custom evals built from your real data, run on every change. We measure factual correctness, helpfulness, latency, and cost. We do not ship a model change without an eval.
◆ Pairs with
AI Product Development usually pairs with our Strategy service.
Validation, roadmaps, and the first three days of any engagement.
Read the Strategy playbook◆ Let's talk
Have an idea?
Let's see if we can make it real together.
Or email us at hello@thinkmakr.com