Custom AI Model Fine-Tuning
We have deep experience fine-tuning compact LLMs to understand coding rules, industry terms and acronyms, follow critical rules, map to fields, and return outputs that receiving systems accept on the first pass.
Fine-tuning works best when we have clean before→after examples and a clear target schema.
We help define your fields, allowed values, and policies, normalize raw inputs, and turn your best examples into high-quality training data with a proper hold-out set.
You get consistent, schema-correct outputs at speed, less cleanup and rework, faster cycle times, cleaner data, auditable decisions, and predictable costs.
Deployment fits your tech stack with real-time and batch paths, dashboards for accuracy and drift, and a retraining playbook so performance improves as new labeled examples arrive.
How we do it
We run a multi-agent pipeline. An ingestion agent gathers data from the channels you use (web, forms, chat, email, files, APIs). A document agent extracts text from PDFs, images, and Office files. A training-data agent profiles sources, maps scenarios, balances edge cases, de-identifies sensitive content, and builds JSONL datasets aligned to your schema and acceptance tests.
A teacher/evaluator agent guides the model toward schema-correct, policy-aware outputs during training. The fine-tuned model then produces structured JSON or drafts. A policy/validation agent enforces JSON Schema and business rules, and routes low-confidence cases to humans. Finally, an operations agent monitors accuracy, drift, latency, and tool-call success and supports safe version rollbacks.
Training data analysis and development
We categorize real scenarios, select the highest-quality examples, and define evaluation rubrics up front. We use curriculum learning to start with common patterns and add edge cases, distillation from a larger teacher to tighten structure and tone, and active learning to focus on the error types that matter to your operations.
Operational AI that Moves Work Forward
Custom AI automation turns scattered, manual steps into a reliable, observable flow—reading what comes in, deciding, executing, and capturing proof so context never gets lost.
Customers get clear, cited answers and a fast route to action (book, form, quote, or handoff). Behind the scenes, systems stay in sync and your team handles exceptions—not copy/paste.
It understands intent across email, forms, chat, and uploads; retrieves facts from approved sources; takes the next step in your tools; and logs each decision for audit and reporting.
Result: faster responses, higher conversion, lower handle time, cleaner data, stronger governance, and happier teams. It plugs into site search, chat, inboxes, forms, calendars, ticketing, CRM, and knowledge stores—turning every touchpoint into a short path from question → answer → action with full visibility.
- Automate repetitive, error-prone tasks
- Cut handle time and response SLAs
- Eliminate copy/paste between systems
- Trigger actions from AI-classified intent
- Keep data clean and synced to your CRM
- Track outcomes with audit-ready logs
FAQ
Q: What is Custom AI Model Fine-Tuning?
A: It’s the process of training a compact large language model (LLM) on your organization’s real data so it understands your terminology, follows your rules, and produces structured, schema-correct outputs that your systems can accept automatically — no cleanup required.
Q: Why would I need a fine-tuned model instead of using a generic one?
A: Off-the-shelf LLMs are generalists. Fine-tuning makes them specialists — fluent in your industry language, data formats, and compliance policies. That means fewer errors, faster turnaround, and predictable performance across repetitive workflows.
Q: What kinds of use cases benefit most from fine-tuning?
A: Scenarios where inputs follow known patterns and outputs must match a fixed structure — such as form processing, claim or quote generation, code or policy validation, CRM data mapping, or AI automation that requires accuracy and auditability.
Q: What makes Trinzik’s fine-tuning process different?
A: We use a multi-agent pipeline that automates ingestion, text extraction, data profiling, and validation. Specialized agents handle training data creation, schema enforcement, evaluation, and drift monitoring — ensuring consistent, policy-aware performance from day one.
Q: What kind of data do you need to fine-tune a model?
A: The best results come from clean “before → after” examples that show ideal outputs and target fields. We help you define the schema, normalize your inputs, and build a balanced dataset with a proper hold-out set for unbiased evaluation.
Q: How do you ensure the model follows our business rules?
A: During training, a teacher/evaluator agent guides the model toward policy-compliant, schema-correct outputs. At runtime, a policy/validation agent checks every result against your JSON Schema and routes any low-confidence cases to human review.
Q: How is accuracy measured and maintained after deployment?
A: Dashboards track accuracy, drift, latency, and tool-call success. When new labeled examples arrive, the system supports retraining or rollback, so performance improves continuously without breaking production workflows.
Q: Can fine-tuned models integrate with our existing systems?
A: Yes. Deployment fits your stack — real-time APIs, batch pipelines, or embedded applications. The model can write directly into your CRM, ticketing, or analytics systems, keeping data clean, synchronized, and traceable.
Q: What outcomes can we expect from fine-tuning?
A: You get consistent, schema-validated outputs, shorter cycle times, reduced rework, lower cost per transaction, and full audit visibility. Teams spend less time fixing AI output and more time acting on reliable results.
Q: How do we get started?
A: Start with a discovery session to identify your structured use cases and available examples. We’ll define your schema, prepare the data, run pilot fine-tuning, and deliver a model that fits seamlessly into your operational flow.