Enterprise Fine-Tuning
Custom-tuned and retrieval-grounded models built on your data — for the accuracy, tone, and compliance a generic model can’t reach.
The problem
Off-the-shelf models don’t know your domain, your data, or your rules. For regulated, high-stakes work, “close enough” isn’t good enough — you need a model measured on your tasks and controlled for your compliance.
What it includes
The capabilities we build into this solution — engineered for production, not a demo.
Fine-tuning
Adapt a base model to your domain, tone, and formats where it earns its keep.
Retrieval grounding (RAG)
Ground answers in your knowledge so they’re current and citable, not guessed.
Evaluation harness
Score every version against a labelled set — accuracy is a measured number.
Data pipeline
Secure ingestion and preparation of your proprietary data.
Guardrails & compliance
Policy, safety, and access controls tuned to your risk profile.
Deployment your way
Your cloud, VPC, or on-prem — wherever your data has to live.
How we'd build it
The same method behind everything we ship: de-risked, measured, and in production in one quarter.
Feasibility
We validate the use case on your real data, define the accuracy bar and ROI, and give you an honest go/no-go — usually in weeks.
Build & evaluate
We engineer the system and score it against a labelled set until it clears the bar — quality is a measured number, not a promise.
Deploy
We ship to production, integrated with your systems and behind the right human-approval gates, with monitoring from day one.
Operate & improve
We watch it in production, handle drift, and expand scope as trust builds — feasibility in 6 weeks, production in ~90 days.
What you get
- A model measured on your tasks, not a generic benchmark
- Retrieval grounding so outputs stay accurate and current
- Guardrails and access controls for your compliance needs
- Deployment into your environment with monitoring
Production AI we've shipped
We'd build your solution with the same discipline behind these real, in-production engagements — described by sector under NDA.
A production document classifier — 5,000+ documents/month, built to a ≥95% accuracy target and live in 8 weeks.
Read the case studyBank-statement automation across 21 banks with a measured 0.24% transaction-direction error rate.
Read the case studyAI-at-POS recommendations running against live in-store transactions across every location.
Read the case studyWant to scope Enterprise Fine-Tuning?
Tell us the use case. We'll come back with a feasibility view and an honest go/no-go — a working prototype in about six weeks.
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Location
Barcelona, Spain • Lisbon, Portugal • Sheridan, WY, USA (incorporation)
Why Choose CONE RED?
- AI R&D lab & venture builder — production systems, not slideware
- Feasibility in 6 weeks, production in ~90 days
- Accuracy measured and evaluated — never invented or guaranteed
- Real engagements across financial services, healthcare & retail
- End-to-end support from strategy to deployment
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