We help technical and operations leaders deploy AI into mission-critical processes: choosing models on your real tasks with statistical error bars, extracting verified data from unstructured files, and shipping workflows with deterministic evaluation gates.
Start at whichever step matches your current state. Most clients begin with an opportunity assessment or a benchmarking pilot.
Find the workflow with the volume, data availability and error tolerance to repay the investment.
Run your real tasks through candidate models with repeated trials and Wilson intervals, so choices are measured rather than guessed.
We engineer modular pipelines with clean API contracts and deterministic evaluation gates, partnering with your team to assist safe integration into your infrastructure.
Monitor quality, cost and new model releases, screening PII and handing over runbooks so your team owns it.
Choose the phase that matches your roadmap. Every offering runs on production-grade pipelines proven on real data.
Before writing production code, we score candidate workflows on volume and error tolerance, and empirically test models with Wilson intervals on your private data.
Identify and rank the workflows worth automating first, sized for your team and error tolerance.
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.
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.
We deliver tested validation logic, containerized services, and clean API contracts, partnering with your internal team to integrate directly into your stack.
Embedded engineering to build, evaluate, and ship one production AI workflow.
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 messy documents, call recordings, tickets, and filings into structured databases—checked line-by-line against the source.
Operational knowledge is trapped across PDFs, recorded customer calls, support tickets, and messy feeds. Models extract fields quickly but frequently hallucinate, misattribute figures, or misinterpret nuances. Without deterministic source attribution, bad data quietly poisons downstream ERPs, billing, and automated decisions.
Vendors release and deprecate models constantly. We re-run your evaluation suites when models update, screen for PII, and maintain full call auditability.
Know whether the new model release is better for you before you switch.
Vendors ship and retire models constantly. Each change can move quality and cost in a live workflow, and most teams find out from their users.
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.
High-scale publishing and content automation pipelines built on verified, first-party data sources rather than ungrounded generative copy.
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.
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.
Automated intelligence briefings 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 core capability gets a worked example on an openly licensed dataset, so you can inspect output, error rates, and validation logic before sharing confidential 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)
Unstructured data extraction and deterministic validation
Extract six headline figures across 46 corporate annual reports with line-level quotes; enforce multi-layer deterministic validation rules; and measure catch rate and review cost against filed SEC XBRL ground truth.
Data: SEC EDGAR full-text filings and the XBRL Company Facts API (public, US government)
Unstructured data extraction and deterministic validation
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)
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.