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[Fine-tuned Models/Agentic Systems/Production Deployments]

Your Data.Custom Models.Real Agents.

We build and fine-tune custom AI models and deploy agentic systems that work for your business — from research to production.

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>Building intelligent systems for what's next.

Custom Models/Agentic Workflows/Enterprise Deployment

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How it works

Four stages, one delivery pipeline.

Most AI pilots die between the notebook and the org. We own the whole path — the data contract, the model, the agent runtime and the deployment target.

$ hawthorn deploy --pipeline production✓ data contract validated · 4 sources✓ model candidate evaluated · +18.6 pts✓ agent guardrails passing · 42 checks→ shipping to 3 regions
01stage

Your Data

  • text
  • documents
  • logs
  • apis

private, governed, yours

02stage

Custom Fine-Tuned Models

  • domain specific
  • higher accuracy
  • better reasoning

trained on your distribution

03stage

Agentic Systems

  • plan
  • reason
  • use tools
  • take action
  • self-improve

grounded, supervised, observable

04stage

Deployment

  • cloud
  • on-prem
  • hybrid
  • scalable
  • secure

inside your pipeline

01 / Capabilities

Four disciplines.
One accountable team.

We are not a staffing shop and not a research lab. We are the engineering layer that turns a model into a system your business can depend on.

01

Custom Model Development

We fine-tune, distill and evaluate foundation models on your domain data until they beat generic ones where it actually matters — with the evals to prove it.

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02

Agentic Systems

Plan, reason and act loops with tools, memory, policy gates and human checkpoints — designed to survive retries, handoffs and messy real workflows.

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03

Enterprise Deployment

Cloud, on-prem, hybrid or air-gapped. Shipped into your CI/CD, observability and security posture instead of living in a demo notebook.

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04

Evaluation & Governance

Trace-level evaluations, red-teaming, provenance and audit trails. Every release ships with evidence, not vibes.

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142Krecords analysedAnomaly detection across a national collection network
+18.6points over baselineTypical lift of a domain fine-tune vs. the base model
3deployment targetsCloud, on-prem and hybrid from one artifact
24hfirst responseNDA-friendly scoping call with an engineer
02 / Where we go deep

Pick the layer you need.

Each workstream is a full engagement on its own — and they compound when you run them together.

A

Custom Models

Domain-adapted models that know your terminology, your formats and your edge cases.

  • SFT, DPO, LoRA & distillation
  • Private data pipelines
  • Eval harness & baselines
B

Agentic Systems

Supervised agents that plan, call your tools, use memory and know when to stop.

  • Tool & MCP integration
  • Memory & retrieval
  • Policy gates and rollback
C

Deployments

The unglamorous part done properly: rollout, scaling, monitoring, cost and compliance.

  • Cloud, on-prem, hybrid, air-gapped
  • Model CI/CD & canary releases
  • Drift, latency and cost SLOs
Real-world signal / deployed

142K records.
One hidden pattern.

A multi-agent anomaly detection system for a multimillion-dollar dairy company — built to surface subtle quality anomalies hiding across a vast collection network that manual sampling never caught.

  • Fuses sensor, collection and quality signals across heterogeneous sources
  • Agents rank and justify every anomaly with traceable evidence
  • Findings surfaced to operations, not buried in a dashboard
Read how we approach it
Evidence log
600–700+collection centres monitored
2 of 3AI-flagged centres confirmed in a secret audit
signalconfirmed
precision @ audit66.7%
false positives1 of 3
statusin production
03 / Engagement

Research to
production.

A fixed-shape engagement with an engineer attached to every stage. No handoff cliff, no proof-of-concept graveyard.

Engagement models
01week 1–2

Discover

We map the workflow, the data you actually have, the failure modes that cost you, and the bar a system must clear to be trusted.

02week 2–5

Prototype

A thin vertical slice in your environment: a fine-tuned candidate or agent loop wired to real tools, measured against a baseline you agree on.

03week 5–10

Harden

Evals, guardrails, adversarial testing, latency and cost tuning, fallback paths and human review points until it holds under load.

04ongoing

Operate

Deployment pipelines, drift monitoring, retraining triggers and on-call runbooks — so the system keeps improving after launch.

Toolchain we ship with
Python/PyTorch/vLLM/Hugging Face/Ray/MLflow/Weights & Biases/LangGraph/Kubernetes/Terraform/Postgres/pgvector/Kafka/AWS/GCP/Azure/Modal/FastAPI/Redis/Airflow/
Custom models/Agentic systems/Production deployment/Evaluation/Governance/Observability/Cost control/Security review/
Start a build

Tell us the workflow.
We'll bring the system.

Share the data, the tools and the bar for success. You get a scoped plan, a timeline and a named engineer — not a deck.

Build Your AI SystemSee Our Solutions

Typical first response within 24 hours · NDA-friendly