Solutions · Use case
Evaluation Harnesses.
Generative AI, scoped to the shape of the problem it solves.
Continuous, automated evaluation pipelines for production generative-AI systems.
Use-case context
Every generative-AI system in production needs an evaluation harness before it can be trusted to ship changes, and most organisations either skip this step or hand-roll a spreadsheet of prompts that goes stale within a sprint. The result is a model that silently regresses on the cases that matter while looking fine on the cases someone happened to test by hand. We build evaluation harnesses that combine labelled gold sets, LLM-as-judge graders calibrated against human judgement, programmatic checks (format, schema, citation grounding), and red-team adversarial suites. The harness runs in CI on every model, prompt, or pipeline change and blocks deploys on regression against the defined quality gates. Meta-evaluated by inter-rater agreement between the harness and human reviewers on a stratified sample, plus the harness's ability to catch regressions in deliberate adversarial replays. The harness itself is treated as a tested artefact, not a one-off script. We do not certify that an evaluation harness is "enough" for a given regulatory regime — we build the harness against the criteria your risk and compliance owners specify, and they retain accountability for sign-off. In our coverage footprint this lands first across financial services, healthcare, legal — the sectors where the data shapes and evaluation criteria line up cleanly with what this use-case actually measures.
How this shows up across industries
Where evaluation harnesses lands in production engagements.
Healthcare · AI Integration Services
See industry →Healthcare integrations live or die on permissioning and audit trails into the EMR — we plan for both up front.
Financial Services · Data Science & Analytics
See industry →In financial services, data-science engagements gravitate toward fraud detection, customer-segment lift, and credit risk modelling — with model-monitoring built in.
Insurance · Data Science & Analytics
See industry →In insurance, data science is the day-job of pricing, reserving, and claims-cost modelling — we extend it with modern tooling and observability.
Data shape
Labelled gold sets curated with your subject-matter experts, historical incident and regression cases, adversarial prompts assembled by red-teamers, and the CI pipeline that already gates deployments.
Delivering services
Where it lands first
Industries that include evaluation harnesses in their applicable use-cases.
- 01
Financial Services
Generative AI for banks, asset managers, and capital markets — under explicit regulatory posture.
- 02
Healthcare
Tightly-scoped generative AI for clinical operations and document automation — never direct clinical decisioning.
- 03
Legal
Citation-grounded generative AI for matter management, document review, and drafting workflows.
- 04
Insurance
Generative AI for claims summarisation, underwriting research, and submission-document classification.
- 05
Technology
Generative AI inside the product, the codebase, and the internal tooling — built by engineers, for engineers.
Where we draw the line
We do not certify that an evaluation harness is "enough" for a given regulatory regime — we build the harness against the criteria your risk and compliance owners specify, and they retain accountability for sign-off.
Talk to us about a evaluation harnesses engagement
A 30-minute call to scope where evaluation harnesses actually moves the curve against your evaluation criteria.
Book strategy callWhy work with Veso AI on evaluation harnesses
Measured
Evaluation, not opinion
Meta-evaluated by inter-rater agreement between the harness and human reviewers on a stratified sample, plus the harness's ability to catch regressions in deliberate adversarial replays. The harness itself is treated as a tested artefact, not a one-off script.
Fixed-fee
After paid discovery
Two-week discovery assembles the labelled evaluation set with your subject-matter experts, then converts into a fixed-fee proposal with explicit gates.
Your repo
Your IP, day one
Code, infrastructure-as-code, evaluation harness, and runbooks land in your accounts — no vendor lock-in on the data, models, or evaluation history.
Related use-cases
Document Intelligence
Citation-grounded retrieval and summarisation over heterogeneous document corpora.
Internal Copilots
Role-shaped copilots over internal knowledge corpora — Confluence, runbooks, policies, code.
Structured Extraction
Schema-conformant extraction of fields, entities, and tables from messy inputs.
FAQ
Evaluation Harnesses — frequently asked questions
How is success measured for evaluation harnesses engagements?
Meta-evaluated by inter-rater agreement between the harness and human reviewers on a stratified sample, plus the harness's ability to catch regressions in deliberate adversarial replays. The harness itself is treated as a tested artefact, not a one-off script. The evaluation harness is part of the deliverable, not an afterthought — we build it during the engagement so your team can run it against the next prompt, model, or pipeline change without us.
Where does Veso AI NOT apply evaluation harnesses?
We do not certify that an evaluation harness is "enough" for a given regulatory regime — we build the harness against the criteria your risk and compliance owners specify, and they retain accountability for sign-off. This is a deliberate trust boundary, not a capability gap — we are equipped to build the systems we decline to build, and we decline to build them because the risk-to-value ratio in those surfaces does not justify it.
Which industries does evaluation harnesses apply to?
In our coverage footprint, evaluation harnesses most commonly lands in financial services, healthcare, legal, insurance. The specific deployment shape varies by industry — data shapes, evaluation criteria, and regulators differ enough that we re-scope each engagement against the sector it lands in.
What data shape do you need to start a evaluation harnesses engagement?
Labelled gold sets curated with your subject-matter experts, historical incident and regression cases, adversarial prompts assembled by red-teamers, and the CI pipeline that already gates deployments. During the paid two-week discovery we map the actual data surface — what exists, what is labelled, what residency posture it carries — and the proposal for the next gate is shaped against that, not against an assumption.
Which Veso AI services ship evaluation harnesses?
evaluation harnesses ships under our Generative AI Consulting, Custom Software Development, AI Integration Services service lines, depending on the integration surface and the build-vs-platform trade-off. Most engagements draw on more than one — the boundary between consulting, custom build, and integration is a scoping decision we make explicit during discovery.
How does a evaluation harnesses engagement typically start?
With a paid two-week discovery: workshops with leadership and operators, an evaluation-set assembled with your subject-matter experts, and a fixed-fee proposal for the next gate. The evaluation set anchors every subsequent decision — model choice, prompt strategy, retrieval design — so quality is measurable from week one, not from go-live.
Industries where evaluation harnesses applies
- Financial Services Generative AI for banks, asset managers, and capital markets — under explicit regulatory posture.
- Healthcare Tightly-scoped generative AI for clinical operations and document automation — never direct clinical decisioning.
- Legal Citation-grounded generative AI for matter management, document review, and drafting workflows.
- Insurance Generative AI for claims summarisation, underwriting research, and submission-document classification.
- Technology Generative AI inside the product, the codebase, and the internal tooling — built by engineers, for engineers.
Service lines that ship evaluation harnesses