- Sheridan, Wyoming, US
- [email protected]
- Call Us Anytime+123 (4567) 890
- Email Us[email protected]
- Opening HourMon - Sat 8:00 - 6:30, Sunday - CLOSED
Get latest update for our trusted applications
Generative AI & LLMs
Large language models are cheap. Getting them to be reliably right is the work.
We build generative AI that operates on your knowledge, inside your tenancy, with citations, evaluation and guardrails — because a confident wrong answer to a customer costs more than the licence ever saved.
−82%
handling timeOn document-heavy intake and review work
96%
extraction accuracyField-level, on unstructured client documents
0
data leavesClient data never used to train external models
Six thousand pages a month, read by three people.
A lending client's underwriting team was reading bank statements, tax filings, valuations and title documents by hand. Six thousand pages in an average month, spread across three analysts, with a four-day turnaround that lost deals to faster competitors and a defect rate nobody wanted written down.
Generative AI was obviously applicable. What made it work was refusing to trust it blindly. Every extracted field carries a citation to the page and line it came from. Anything below a confidence threshold is routed to a human with the source highlighted. The evaluation suite runs on a labelled set before any model or prompt change ships.
Turnaround went from four days to under four hours, the defect rate fell, and the analysts stopped transcribing and started underwriting. Same three people, a materially larger book.
The citations were what got it past our credit committee. They could check the machine's homework.
Where generative AI earns its keep in real operations.
We deploy generative AI against document volume, knowledge access and content throughput — the three places where language work quietly consumes payroll.
Document intelligence
Classify, extract, validate and reconcile fields from contracts, statements, invoices, claims, titles and forms — with a citation for every value.
Enterprise knowledge assistants
Answer staff and customer questions from your policies, tariffs, manuals and product data — grounded, cited, permission-aware and always current.
Drafting & summarisation
Case notes, credit memos, service responses, inspection reports and handover summaries drafted from source data for a human to approve.
Proposal & content operations
Generate compliant proposals, listings, product copy and multilingual variants from structured inputs and an approved brand voice.
Code and data copilots
Internal copilots grounded in your codebase, schemas and runbooks, accelerating engineering and analytics work without exposing your IP.
Evaluation & guardrails
Labelled evaluation sets, regression testing, prompt versioning, jailbreak resistance and refusal behaviour defined before launch.
The business effect of language work at machine speed.
Each of these is written into the engagement as a number with an owner, a baseline and a review date.
OUTCOME 01
Throughput without headcount
Document and knowledge work scales with volume rather than with hiring, which changes the unit economics of growth in lending, insurance, property and healthcare administration.
OUTCOME 02
Turnaround becomes a selling point
Decisions in hours instead of days win business outright in competitive markets. Several clients now advertise the turnaround the AI made possible.
OUTCOME 03
Institutional knowledge stops walking out
When the answer lives in a grounded assistant rather than in one long-serving colleague's head, onboarding gets faster and service quality gets less personality-dependent.
Generative AI you can put in front of a regulator.
- Private deployment in your cloud with no external training on your data
- Retrieval layer over your approved, permissioned corpus
- Citation and confidence surfaced in every output
- Labelled evaluation suite plus regression gates on every change
- Human review workflow for low-confidence and high-risk cases
- Cost-per-document and cost-per-answer reporting
The engineering underneath.
Numbers from work already in production.
Measured against the baseline agreed with the client before the engagement started.
4 days → 4 hrs
Underwriting turnaroundDocument intelligence in specialist lending
6,000
Pages processed monthlyPreviously read manually by three analysts
96%
Field-level accuracyWith citation and confidence routing
What risk, legal and IT teams ask.
- 01. Will our data train someone else's model?No. We deploy in your tenancy with training and retention disabled on the provider side, and this is written into the contract rather than left to a settings page.
- 02. How do you stop hallucination?By constraining answers to retrieved, approved content, requiring citations, thresholding on confidence and routing anything uncertain to a human. Where a claim cannot be grounded, the system says so instead of inventing.
- 03. Which model do you use?Whichever wins your evaluation set at acceptable cost and latency. We build model-agnostic so that when the frontier moves — and it moves every few months — you switch without rebuilding.
- 04. Can it run without internet access?Yes. For clients with strict residency or air-gap requirements we deploy open-weight models entirely inside their own infrastructure.
Where to go next.
Show us the pile of paper. We will show you the cost of reading it by hand.
The assessment samples your real documents, measures achievable accuracy and models the value of the turnaround you would gain.
