It answers from your data. And it proves the answer is right.
A grounded feature retrieves the right context, stays inside your documents, and is scored against a real test set before a customer sees it. You launch on an accuracy number, not a gut feeling - and the model tells you when it is unsure instead of guessing.
Everything around the model, which is the hard part.
An Applied AI engagement turns a promising prototype into a feature your users can depend on. The model is the easy part. The work is getting the right context in front of it, keeping it grounded in your data, and proving the output is correct before a customer sees it.
- Retrieval that returns the right thing. Chunking, embeddings, and a retrieval strategy tuned to your actual documents, not a generic tutorial setup.
- Prompt and context engineering. The system prompt, the context window budget, and the guardrails that keep answers on-topic and grounded.
- A real endpoint. Wired into your stack with error handling, fallbacks, and rate limits - the parts a demo skips.
- Evals before launch. A test set that tells you the accuracy number, so you ship on evidence instead of vibes.
The same grounded pipeline, across very different floors.
Any organization sitting on documents, records, or knowledge hits the same wall: a generic model that sounds confident and gets the specifics wrong. Applied AI is how that knowledge becomes an answer a user can actually trust.
The manual nobody can search
Decades of machine manuals, safety procedures, and maintenance logs sit in PDFs. A grounded assistant lets a floor technician ask a plain question and get the exact, cited answer instead of paging through a binder.
The literature that outpaces the team
Clinical and research groups drown in papers and guidelines. Retrieval over their own vetted corpus surfaces grounded, sourced answers - and stays inside the documents they trust instead of inventing studies.
The policy maze a citizen can't navigate
An agency or nonprofit holds the rules people need but cannot find. A grounded assistant answers from the actual regulations, with citations, so staff and the public get a reliable answer instead of a guess.
Retrieving the right document is half the job. Proving the answer stuck to it is the other half.
Most grounded-AI builds stop at retrieval: pull the relevant document, hand it to the model, hope it stays faithful. It usually does - until the one time it quietly blends a real source with an invented detail, and a confident wrong answer reaches your user. The maturing fix is a faithfulness check: verify each claim against the source before it ships.
Find the right passage
Pull the specific, vetted source that should answer the question - the part most builds get to and stop at.
Answer, sourced
Every claim carries the exact passage it came from, so a person can check the receipt instead of trusting the tone.
Catch the drift
A second pass checks each statement actually follows from its cited source and flags or blocks the ones that do not - the guard that stops a confident hallucination at the door.
I build the faithfulness check in by default, so a grounded assistant is verified against its own sources rather than just sounding right - a step most teams shipping RAG for small operators still skip. Related: why reliability and cost are the same problem →
Fixed scope. Async. One payment after the audit.
- Scope and audit. You send your stack, your data shape, and the feature you want. I send back a fixed price and a written plan within 24 hours, or a straight no.
- Build the pipeline. Retrieval, context assembly, and the model call - against your real data, in your real environment.
- Evaluate. I build the eval set and run it, so we both see the accuracy and the failure modes before users do.
- Ship and hand off. Live endpoint, monitoring, and a written runbook so your team can run and extend it.
I have shipped multi-vendor LLM systems into regulated production environments under hard deadlines, including a compliance-bound agent delivered in 3 days. See the builds →
If a grounded, trustworthy AI feature lets you convert even 2 of every 10 trial users you currently lose to "it gave me a wrong answer," and each one is worth $1,000 a year, that is the engagement paid back many times over in the first quarter.
Tell me what is breaking
Send me your stack, your data shape, and the one feature you most need to trust in production. Within 24 hours you get a free written teardown of it - what I would build, what it would take, and a fixed price - or a straight no.
Get my free teardown →