AI Solutions
Generative AI, RAG systems, OCR, and predictive analytics — grounded in your data, wired into your product, and evaluated honestly before anyone calls it done.
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Trusted by teams across education, retail, and services
{ 01 } — AI process
From demo to dependable.
The gap between an AI demo and production is engineering discipline: grounding in your data, an evaluation suite that exists before launch, and confidence thresholds that know when to hand a case to a human. That gap is our specialty — and it is where most pilots quietly die.
Ground
- Use-case framing with a measurable definition of “good”
- Data audit — what exists, where it lives, how clean it is
- Retrieval design: RAG over your documents, not open-web guesses
- Model & provider selection by evaluation, not fashion
- Privacy, residency & compliance constraints mapped up front
Engineer
- LLM integration with versioned, testable prompts
- Golden-set evaluation suite built before launch
- Confidence thresholds with human-in-the-loop routing
- Output schemas & guardrails on every response
- Cost-per-query budgets designed in, not discovered on the invoice
Operate
- Quality scored against the eval suite on every change
- Drift & regression checks as models and data move
- Cost tracked per query and per feature
- Feedback loops from real usage into prompts and retrieval
- Model swaps when the market moves — evals prove the swap
Pilots stall because nobody defined what good means. We build the evaluation suite first — golden questions with known-good answers, accuracy thresholds, escalation rules — so shipping is a measurement, not a feeling. If the numbers are not there, you find out in week three, not month six.
Grounding comes second. Answers cite their sources, retrieval is scoped to your documents, and low-confidence cases route to a person with context attached. A system that says “I don’t know” at the right moments is worth more than one that always answers.
We build to one argument on every engagement: evaluation designed first, capabilities second, autonomy last. Our own RAG assistant runs on exactly this pattern — we will walk you through it on a call so you can check the citations yourself.
{ 03 } — What we build
AI that survives contact with production.
RAG & knowledge assistants
Answers from your documents with citations attached — ask for a walkthrough and follow a source back yourself.
LLM product features
Drafting, summarizing, classification, and extraction wired into your existing product — behind your UI, on your data, under your schemas.
OCR & document intelligence
Invoices, forms, IDs, and scans turned into structured, validated data — with a confidence score on every field and a human queue for the doubtful ones.
Predictive analytics
Forecasts and scoring models that improve decisions — with honest error bars and a documented baseline they have to beat.
Semantic search
Search that understands meaning, not just keywords — across products, documents, and support archives, tuned on your real queries.
AI audits & rescues
Second opinions on stalled AI projects — where the pilot went wrong, what an eval suite says about it, and whether it is worth saving.
{ 04 } — Solution stack
Model-agnostic, by policy.
The right model per task, swapped when the market moves — your solution should not be married to one vendor. The grounding, evaluation, and guardrail layers are ours and stay constant; the model underneath is replaceable.
{ 05 } — Ways to engage
Three ways to start, matched to your risk appetite.
Scoped pilot
One use case, fixed price, 4–6 weeks. Production data, real users, and an evaluation suite from day one — ends with a working tool and a keep-or-kill decision backed by numbers.
- Fixed scope and price
- Production data, real users
- Success metric agreed before code
Build + handover
We design and build the full solution — retrieval, evals, guardrails, monitoring — train your team, and hand over the keys to your infrastructure.
- Your infrastructure, your keys
- Prompts, evals & docs included
- Support after handover
AI partner retainer
Ongoing capacity for teams shipping AI features every quarter — prompts maintained, models re-benchmarked, evals watched as the ground shifts.
- Monthly capacity, no re-scoping
- Model + prompt maintenance
- Evals watched continuously
{ 06 } — What you receive
The solution, and the evidence it works.
An AI feature without its evaluation suite is an opinion. Every engagement ships the system and the artifacts that keep it honest a year later.
The problem framed with a measurable definition of done — and an honest note on what AI cannot do here yet.
The working solution wired to your data and your product, deployed in your infrastructure or cloud tenancy.
Golden questions with known-good answers, scored on every change of prompt, model, or retrieval — quality as a number, not a feeling.
Every prompt versioned, every model choice documented with the eval results that justified it — so “why does it do that” has an answer.
Cost per query, per feature, per month — the finance conversation becomes a screenshot, not an estimate.
Failure modes, escalation paths, retraining triggers, and team training — operating it never depends on us.
{ 07 } — The symptoms
Signs an AI solution would earn its keep.
AI pays off where the knowledge already exists, the volume is real, and “good” can be defined precisely enough to measure.
{ 08 } — What changes
From plausible demo to accountable system.
Before
A chatbot that guesses when it doesn't know.
After
Answers with citations — and “I don’t know” routed to a human with context attached.
Before
Quality judged by whoever tried it last.
After
Quality scored against a golden set on every release — regressions caught before users are.
Before
AI spend discovered on the invoice.
After
Cost per query budgeted, tracked, and routed to the cheapest model that passes evals.
Before
The pilot lives in a notebook on one laptop.
After
A deployed system in your infrastructure — versioned, monitored, documented.
Before
Married to one vendor's model and pricing.
After
Model-agnostic by design — swapped when the market moves, with evals proving the swap.
Where this applies
Get expert guidance on your AI use case.
Book a free consultation call — a senior team member replies within one business day with real thoughts, not a sales script.
Frequently asked questions
The right one per task and budget — commercial APIs or open-weight, chosen by evaluation results on your data, not by fashion. The choice is documented, and revisited when the market moves.
Your data stays in your infrastructure or your cloud tenancy; we design for data residency and never train shared models on client data — the same commitment our privacy policy makes in writing.
A golden-set evaluation suite built before launch — real questions with known-good answers, scored on every change of prompt, model, or retrieval. Low-confidence answers route to humans, and the routing itself is part of the measurement.
Yes — we maintain a working RAG assistant and an AI workflow build, both showing citations and human-in-the-loop routing, and we demo them on consultation calls. They run the same patterns we ship to clients.
A scoped pilot reaches real users in 4–6 weeks — production data, an agreed success metric, and a keep-or-kill decision at the end. Anything promising results faster is usually skipping the evaluation.
Then we say so, in writing. The framing stage exists to kill weak use cases cheaply — sometimes the honest recommendation is a rules engine, a better form, or nothing at all. “Not yet” in week two costs far less than in month eight.