Training Data for Enterprise Copilots
Enterprise copilots need domain-grounded training and evaluation data so they can answer accurately inside regulated, proprietary, and workflow-specific contexts. InfoBay curates and evaluates data for legal, finance, healthcare, STEM, and business copilots.
The problem
Enterprise copilots fail in a specific way generic chat models don't: they sound confident while citing a policy that doesn't exist, misquoting a regulation, or blending two internal workflows into one wrong answer. That failure mode isn't fixed by more general instruction data — it requires domain-grounded training and evaluation data drawn from the actual regulated, proprietary, and workflow-specific material the copilot has to be right about. The fix isn't a bigger model or a longer system prompt — it's training and evaluation data that was actually grounded in the domain the copilot operates in. InfoBay curates and evaluates data for legal, finance, healthcare, and business copilots so domain accuracy is measured, not assumed.
Corpus assets & provenance
Non-STEM textbooks for regulated-domain grounding
+Copilots operating in law, finance, and healthcare need factual grounding that ordinary web text doesn't provide. InfoBay's textbook corpus includes non-STEM domains — law, business, healthcare — within its 42K+-book, 15-language catalog, each title ISBN-attributed rather than web-scraped, so a copilot's regulatory or clinical references can be traced back to a published source instead of an uncredited web page.
Expert preference data for domain calibration
+Generic RLHF preference rankings don't catch a subtly wrong contract clause or an outdated tax rule. InfoBay's Q&A corpus (6.7M+ pairs) is built with explicit reasoning traces from domain SMEs, and the same expert pool supports preference annotation for RLHF calibration — attorneys ranking legal-copilot outputs, finance professionals ranking financial-copilot outputs — so alignment reflects domain judgment rather than generic helpfulness.
Factuality evaluation before and after deployment
+LLM factuality evaluation catches hallucinated citations, unsupported claims, and domain-specific errors that generic QA misses. For enterprise copilots this means testing against the workflows the copilot will actually run — contract review, claims triage, clinical documentation — instead of a generic benchmark that doesn't reflect the deployment surface, and re-testing after every material training update.
Provenance for procurement and compliance review
+InfoBay's corpus is collected via formal enterprise agreements, not web-scraped, with textbook ISBN attribution and SME-authorship metadata structured for review under EU AI Act Article 10, India DPDP Act, CCPA. For copilots deployed in regulated workflows, GDPR-eligible lineage gives legal and procurement teams a documented trail before the data enters training or evaluation.
Scoped by workflow, not just by domain
+A legal copilot reviewing NDAs and a legal copilot triaging litigation discovery need different grounding data even though both are "legal AI" — the failure modes, source documents, and expert reviewers differ by workflow, not only by industry vertical. InfoBay scopes textbook, Q&A, and evaluation data to the specific workflow a copilot runs, rather than treating an entire regulated domain as a single undifferentiated training target.
Specifications
| Corpus | Format | Volume | Domain coverage | Provenance |
|---|---|---|---|---|
| Non-STEM textbooks | JSONL (gzip) + Parquet metadata manifest | 2.8B+ words across 15 languages | Law, business, healthcare, and more | ISBN-attributed, licensed |
| Q&A + expert preference data | JSONL, structured explanation and ranking fields | 6.7M+ pairs | Cross-domain, SME-authored | Domain-expert reviewed |
| Factuality evaluation | Structured error taxonomy + reviewer rationale | 1,000+ scored responses per review cycle | Legal, finance, healthcare, enterprise workflows | Expert reviewer sign-off |
How an engagement works
- 1
Baseline against your copilot's real failure modes
Every engagement starts by testing the copilot on its actual deployment workflows — the contracts, claims, or clinical notes it will see in production — rather than a generic benchmark.
- 2
Scope domain corpus and expert reviewers
Textbook, Q&A, and preference data are scoped to the regulated domain and languages the copilot operates in, with the expert reviewer pool matched to that domain.
- 3
Deliver, evaluate, and re-baseline
Data and evaluation findings ship together, measured in factuality improvement percentage and eval pass rate against the original baseline.
Answers for buyers
FAQ
What makes enterprise copilot training data different from general instruction data?+
It's grounded in the regulated or proprietary domain the copilot operates in — non-STEM textbooks, domain Q&A, and expert preference data — rather than generic web-scale instruction tuning.
Can InfoBay evaluate an existing copilot instead of just supplying training data?+
Yes. LLM factuality evaluation is typically run alongside data curation to identify hallucination and domain-accuracy gaps before and after a training update.
Which domains does the textbook corpus cover for copilots?+
Law, business, and healthcare are represented within the 15-language, 42K+-book textbook catalog, alongside STEM subjects.
Who reviews copilot outputs for domain accuracy?+
Domain subject-matter experts — for example attorneys for legal copilots or finance professionals for financial copilots — rather than generalist crowdworkers.
Is the textbook and Q&A data web-scraped?+
No. Textbooks are ISBN-attributed and licensed; Q&A data carries explicit reasoning traces authored by domain SMEs.
How is compliance documentation handled for regulated-domain copilots?+
Provenance is documented per source — ISBN for textbooks, SME authorship for Q&A — structured for review under EU AI Act Article 10, India DPDP Act, CCPA.
Can training data be scoped to a specific copilot workflow rather than a whole domain?+
Yes. Data is typically scoped to the specific workflow — contract review, claims triage, clinical documentation — rather than treated as one undifferentiated domain.
Next steps