AI Agent & Model Training
We build AI agents that take real action, not just chatbots that answer questions. Fine-tuned models, retrieval-grounded reasoning, and scoped tool access come together into agents your team can actually trust with production workflows.
0%
Avg. Task Automation Rate
6-12 wks
Typical Engagement
Human-in-loop
Guardrail Checkpoints
Weekly evals
Model Iteration Cycle
What's Included
Custom LLM Fine-Tuning & RAG
Autonomous Agent Workflows
Evaluation, Guardrails & Monitoring
How We Work
01
Use-Case Scoping
We identify the workflows worth automating and the guardrails an agent needs before writing any training code.
02
Data Curation & Fine-Tuning
Domain data cleaned, labeled, and used to fine-tune or RAG-ground the base model for your context.
03
Agent Orchestration
Tools, memory, and multi-step reasoning wired together so the agent can take action, not just respond.
04
Evaluation & Guardrails
Automated eval suites and human-in-the-loop review gates before anything runs unsupervised.
What You'll Receive
Concrete outputs you walk away with when the engagement wraps — not just a status update.
Fine-tuned or RAG-grounded model
Agent orchestration layer with scoped tool access
Evaluation harness with benchmark scores
Guardrail and escalation-rule documentation
Deployment and monitoring runbook
How Engagements Typically Work
Every project starts with scoping — here's the shape most engagements for this service take.
Use-Case Pilot
A focused pilot on one high-value workflow to prove the agent pattern before scaling to others.
Agent Build & Fine-Tuning
Full engagement covering data curation, fine-tuning or RAG grounding, and orchestration — typically 6-12 weeks.
Evaluation & Guardrail Retainer
Ongoing monitoring, eval-suite maintenance, and guardrail tuning as usage and edge cases grow.
Technologies We Use
The tools and platforms our AI Agent & Model Training team works in day to day.
Languages
Agent Frameworks
Models & Vector Stores
Evaluation & Tooling
Industries We Apply This To
Frequently Asked Questions
Almost always fine-tuning or RAG on top of a strong foundation model — training from scratch rarely makes economic sense outside highly specialized domains.
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