AI Features & RAG for Your App: LLMs, Vision and Search Over Your Data
I add AI features to working products: LLM features with checked output, vision and document extraction, and answers grounded in your own documents (RAG) with citations. It’s for small teams who want AI that is tested, not a demo. From $1,600 ($1,200 founding) for one feature, about 3–4 weeks. You pay model costs directly.
See a sample report, proposal and weekly update- Price
- from $1,600
- Founding
- from $1,200
- Timeline
- 3–4 weeks
- Format
- Fixed scope, fixed price
- Hours
- US Eastern Time, async
Who this is for.
A good fit if
- SaaS and internal-tool teams adding one clear AI feature (write, classify, extract, answer)
- Businesses with documents, manuals or tickets they want searchable with cited answers
- Teams whose AI prototype gives inconsistent or made-up output
Common triggers
- Staff spend hours writing, sorting or copying data from documents
- Customers ask the same questions your docs already answer
- A ChatGPT prototype works in a demo but breaks on real input
- You need answers you can check against a source, and customer data kept private
Not a fit if
- You want a self-hosted model on your own GPUs
- The AI would make final decisions on health, legal, credit or hiring with no human review
- You want a guaranteed accuracy percentage before any testing on your data
What I do, and what you get.
What I do
- Define what “good output” means with you, as a written spec and a test set
- Build the feature with structured output, validation and retries, so bad output is never saved
- Write the rules the model must follow, including what it must never claim
- For RAG: chunk and index your content in PostgreSQL (pgvector), add keyword + vector (hybrid) search, return answers with citations, and say “not found” when the sources don’t cover it
- Measure retrieval and answers against your test questions, before and after changes
- Add cost controls: skip unchanged items, cache, limits per user
What you get
- The feature in your app (or as a small service), with tests that fake the model
- A test set and an evaluation report on your own data
- The prompts and output schemas, versioned in the repo
- A cost note: tokens per request and a monthly estimate at your volume
- Handover notes and a 14-day fix window
Your part
- Model provider accounts and API costs
- 20–50 real examples or questions with correct answers
- A person who reviews output during testing
- Decisions on what data the AI may see
Included
- One scoped feature, or RAG over agreed sources
- Structured output, validation and retries
- An evaluation set and report
- Citation and “not found” behavior (RAG)
- A human review step where output is published
Not included
- Model and API costs
- Self-hosted models and fine-tuning
- Accuracy guarantees
- Data cleanup and content writing
- Legal review of AI use in your sector
Fixed prices, agreed before work starts.
One LLM feature: structured output, validation, retries and tests.
RAG over up to 2 sources, pgvector hybrid search, citations and an eval set. 2 monthly milestones of $1,440 ($1,080 founding).
Vision/document extraction, multi-stage pipelines or several sources. 3 monthly milestones of $1,600 ($1,200 founding).
Build your scope
- Timeline
- 3–4 weeks
- Payment
- 50% to start ($800), 50% before handover
- Earliest start
- Oct 5
Founding price: 3 of 7 spots left, until Mar 31, 2027 — one-off work, in return for a testimonial. How it works
Prices are in USD, paid by bank transfer against an invoice. Work under $1,000 is paid upfront; larger work is 50% to start and 50% before handover, or monthly milestones. How working together works
Variations
- LLM feature in an existing app
- Writing, classification or extraction added to your Laravel, Python or Go code.
- RAG over your docs or data
- pgvector first; a separate vector database (Qdrant) only when size or filtering needs it.
- Vision and document extraction
- Photos, scans or PDFs turned into structured records, with fields the model may not guess.
- AI in an internal tool
- Staff upload a file, review the AI output and approve; nothing publishes without review.
How it runs.
- 01
Brief and sample data
You describe the task and send sample inputs.
- 02
Spec and test set
I write the spec and a test set with you.
- 03
Prototype and first eval
I prototype on your data and run the first evaluation.
- 04
Build and re-evaluate
I build it into your app and evaluate again.
- 05
Handover with cost note
You get the handover pack, the cost note and a 14-day fix window.
Where I have done this before.
AI Listing Pipeline for a Luxury-Watch Auction House
LLM with structured output, validation and retries; 1,027 listings written since May 2026, and an incomplete listing is never stored.
Read the case study →Multimodal AI · Auction catalogingAI Cataloging System for Fine-Jewelry Auctions
Two-stage Gemini vision system; the model is forbidden from guessing stone counts, carats or metal purity.
Read the case study →The assistant on this site is one.
- Grounded: every answer is built from the same data as these pages — services, prices, case studies — so it can’t drift from the site.
- Guardrails: it is told never to invent prices, clients or project details, and to hand exact quotes to a written proposal.
- Validated output: when it recommends a service, the site checks the slug against the real catalog before showing a button.
- Behind the scenes: each inquiry gets a private AI summary for me — the service it fits, missing details and questions to ask back.
- Privacy: no chat history is stored on this site, and no cookies are set.
Questions buyers ask.
Have you built RAG before?
My production AI work is LLM and vision pipelines. RAG uses the same discipline (checked output, tests, human review), and I measure retrieval on your data before you rely on it.
Which model?
Whatever tests best on your data within budget: OpenAI, Anthropic, Google Gemini, or several through one gateway.
Will it make things up?
It can. I reduce it with sources, citations, strict schemas and “not found” answers, and I measure it. I don’t promise zero.
Is our data used to train models?
Business API terms of the major providers exclude training by default; we’ll pick settings together.
Do we need a vector database?
Usually not. PostgreSQL with pgvector covers most small-business sizes.
Not sure where AI fits?
Start with an AI Opportunity Assessment (from $480). Its fee is credited toward the build.
Ask about AI Features & RAG.
Tell me what you have and where it hurts. I reply within one US business day with either a fixed quote or the entry step that makes a fixed quote possible.
Helpful to include
- What the AI should do
- A sample input and the ideal output
- Data sources and volume
- Current stack
- Who reviews output
- Model preference
- Budget for API costs
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