What Born builds
Five capabilities.
One complete stack.
Every service maps to a concrete deliverable. No consulting decks, no vague "AI strategy". You get working artifacts: model weights, datasets, eval harnesses, and running workflows.
Every engagement has defined success criteria
Every deliverable is documented
Honest measurement before and after
Model Adaptation & Fine-Tuning
Take a foundation model. Make it yours.
We adapt pre-trained language models to specific domains, tasks, and output requirements using LoRA, QLoRA, and full-parameter fine-tuning. Every engagement starts with task framing and ends with a packaged, documented checkpoint.
Deliverables
LoRA / QLoRA adapter weights
Training config and run notes
Base model provenance documentation
Merged checkpoint (optional)
Deployment recommendation
Dataset Engineering
Training data built for real performance.
We design and build task-specific datasets from scratch: synthetic generation, teacher model distillation, human-curated examples, quality filtering, and YAML-manifest provenance. The dataset is a first-class artifact, not an afterthought.
Deliverables
JSONL training dataset
YAML provenance manifest
Quality rubric and scoring notes
Train / eval / test splits
Deduplication report
Evaluation Systems
Measurement that reflects actual work.
We build evaluation harnesses that test what matters for your use case — not generic leaderboard proxies. Baseline measurement, failure mode analysis, judged evals, pass/fail harnesses, and a release-ready claims review before you ship.
Deliverables
Task-specific eval harness
Baseline vs fine-tune comparison report
Failure mode taxonomy
Judged eval results (LLM or human)
Release claims checklist
Agent Workflows
The model is one part. We build the rest.
We design and implement multi-step AI workflows: tool calling, verification loops, state management, and recovery paths. Agent systems that are reliable in production, not just in demos.
Deliverables
Agent workflow implementation
Tool integration (APIs, code execution, search)
Verification and error recovery logic
Observability hooks
Deployment and monitoring plan
GPU Training & Inference Setup
From raw hardware to a running experiment.
We set up and manage remote GPU training environments: RunPod, Lambda, or your own cluster. CUDA compatibility, HuggingFace Trainer or Unsloth configuration, VRAM budgeting, and artifact packaging for each run.
Deliverables
GPU environment configuration
Training script setup and validation
VRAM budget analysis
Run monitoring and checkpoint management
Packaged output artifacts
How it works
What to expect.
Scoping call
We understand your task, constraints, and success criteria before quoting anything.
Baseline audit
We measure what the base model can already do — this anchors everything that follows.
Proposal & plan
A written technical proposal with methodology, timeline, and deliverable list.
Execution
We build the data, run the training, measure the results — with weekly written updates.
Delivery & handoff
Artifacts packaged with documentation, run notes, and post-delivery Q&A included.
Ready to build something that actually ships?
Start with a scoping call. We'll tell you honestly whether we can help, what it will take, and what to expect.