SERVICE — 003 / MODEL ENGINEERING
Model Engineering
Fine-tuning, evaluation harnesses, and prompt systems measured against real tasks — not vibes. We make models cheaper, faster, and more accurate on the work that matters to you.
// WHAT YOU ACTUALLY GET
From base model to a measured, shipped specialist.
Data
Dataset curation
Synthetic data generation
Labeling pipelines
Train / eval splits
Training
Fine-tuning (LoRA)
Train / eval splits
Evaluation
Task-specific evals
Regression suites
LLM-as-judge
Human review loops
Serving
Quantized model selection
A/B rollout
Drift monitoring
// ANATOMY OF A MODEL PIPELINE
How a base model becomes a measured specialist.
Real + synthetic, split clean
LoRA, distillation, preferences
Scored against real tasks
Beat the model you have
Quantized, A/B rolled out
Drift, cost, regressions
TRAINING STACK
GPU training
cloud or on-prem
Experiment tracking
runs & metrics
Eval harness
task scores
Model registry
versioned artifacts
Serving layer
low-latency inference
// THE ENGAGEMENT
From first call to shipped system.
01
Map the system
We start with architecture, not prompts. Where data lives, what has to be reliable, and what "done" actually means.
02
Build the stack
API, retrieval, models, and infrastructure assembled as one coherent system — not a notebook glued to a UI.
03
Harden & evaluate
Evals, observability, and failure modes. Reliability engineering applied to non-deterministic systems.
04
Ship & operate
CI/CD, monitoring, and a real maintenance path. The system goes live — and stays live.
START SMALL
Not sure it's even an agent problem yet?
Begin with a fixed-scope discovery sprint. You walk away with a real architecture, a build plan, and an honest read on feasibility — yours to keep, whether or not we build it together.
Let's build something that actually ships.
Tell us what you're trying to build. We'll tell you straight whether — and how — agentic systems get you there.
Start a conversation