We help teams put AI into the processes they already run: choosing the model on their own tasks, building the workflow inside their stack, and validating what it produces before anyone relies on it.
Start at whichever step you have reached. Most clients begin with an assessment or a benchmarking pilot.
Find the workflow with the volume, the data and the tolerance for error to repay the work.
Run your real tasks through candidate models with repeated trials, so the choice is measured rather than guessed.
An engineer embedded with your team ships the workflow into your stack, with validation and a release gate.
Monitor quality, cost and new model releases, and hand over runbooks so your team owns it.
Each service runs on a pipeline we use to publish this site, so what we deliver has already been run on real data.
Two weeks to find the workflow worth automating first, sized for small and mid-sized teams.
Teams are told to use AI and handed a list of tools. What they need is to know which of their own processes has the volume, the data and the tolerance for error to repay the work.
An engineer inside your team who ships one AI workflow to production.
Most AI projects stall between a promising demo and a system people rely on. The gap is integration, evaluation and operations work that an internal team rarely has spare.
Turn documents into structured data with AI, and check every value against its source.
Models extract fields from documents quickly and occasionally invent them, or take a real number from the wrong line. A wrong price or figure that looks plausible is the error nobody catches until it costs money.
Choose the model on your own tasks, with error bars, not on a leaderboard.
Every AI workflow needs a model, and the choice is usually made from a public ranking or a one-shot demo. Neither measures your work, and neither says when two models are really tied.
Find the demand your competitors capture and you do not.
Search is splitting between classic results and AI answers. Content teams guess what to write, and generated content without evidence gets ignored or penalised.
Know whether the new model release is better for you before you switch.
Vendors ship and retire models every few weeks. Each change can move quality and cost in a live workflow, and most teams find out from their users.
Narrated, captioned and dubbed video generated from your verified data.
Explainer, product and report videos are slow to produce and go stale. Fully generated video is fast, but its narration states numbers nobody checked.
Use AI on business data without sending what should stay inside.
Legal and security teams block AI projects because nobody can say where data goes, what was sent, or who approved it.
A daily or weekly briefing on your market, every claim linked to its source.
Analysts spend hours reading the same feeds to write a summary, and AI summaries of the news repeat stories and lose their sources.
Every service gets a worked example on an openly licensed dataset, so you can see the output before sharing any of your own data.
AI consulting and opportunity assessment
Score every task of a chosen occupation for AI suitability, weight by employment and wages, and produce the shortlist an assessment would.
Data: O*NET task statements and BLS Occupational Employment and Wage Statistics (public domain)
Forward-deployed AI engineering
Classify free-text requests to the responsible agency, draft the acknowledgement, escalate low-confidence cases to a person, and gate each release on an evaluation set.
Data: NYC 311 service requests (NYC Open Data)
AI document processing and data validation
Extract six headline figures from ten companies' 10-Ks, each with the statement row it came from; check the row against the filing and send failures to a person; then grade every figure against the XBRL facts the company filed, to measure how far the check can be trusted.
Data: SEC EDGAR full-text filings and the XBRL Company Facts API (public, US government)
Client task benchmarking and model selection
Route real customer messages from the BANKING77 dataset to six queues and to 77 intents across three models; report ties, cost per task and the precision reached.
Data: PolyAI BANKING77, 13,083 real banking customer messages (CC BY 4.0)
Client task benchmarking and model selection
Ask each model whether a contract contains a given clause and quote it; grade against the expert labels and the quoted span.
Data: Atticus Project CUAD, 510 commercial contracts with 41 expert-labelled clause types (CC BY 4.0)
AI search visibility and demand intelligence
Read competitors’ ranked keywords, classify the gaps against the catalogue, and decide what to publish next.
Data: DataForSEO Labs and Google Search Console for terranettechnologies.com
Model upgrade monitoring and regression testing
Re-run the BANKING77 support-routing tasks on every new candidate model and publish a changelog of what moved and whether it is separable.
Data: The published BANKING77 task sets
Grounded AI video production
Build a data packet from the release, generate and validate the script, render data cards, narrate in English and Spanish, and publish with captions.
Data: BLS Employment Situation release and its published tables (public domain)
Automated, cited research briefings
Collect the week’s proposed and final rules, summarise what changed and who it affects, and cite every document.
Data: The Federal Register API, filtered to one agency (public domain)
Read how we test and verify in our methodology.