Value that compounds, watched in real time.
Deployed is the start, not the finish.
How we keep AI healthy.
For teams with AI already in production who need it to stay accurate, affordable, and improving as the world moves.
Performance monitoring
Quality, latency, and success tracked in production.
Drift detection
Data and model drift caught before it costs you.
Cost & routing optimization
Model routing and caching to control spend as volume grows.
Continuous improvement
Prompt, model, and tool iteration on real usage.
Incident response
Alerting, rollback, and human escalation paths.
Retraining pipelines
Scheduled and triggered model refreshes.
What runs underneath production AI.
Keeping AI healthy in production is an engineering discipline with its own stack — from the infrastructure it runs on up through serving, observability, and the governance that spans all of it. This is what we operate for you.
Two loops that keep models honest.
A release pipeline gets a model into production safely; a monitoring loop keeps it accurate once it is there. We build and run both.
Every change is built, tested, and evaluated against a held-out set before it is registered, staged, and rolled out to a slice of traffic — with automatic rollback if the metrics regress.
We monitor inputs and outputs for drift; when it crosses a threshold we alert, diagnose, retrain a candidate, and prove it beats the current model before promoting — a closed loop that recovers accuracy automatically.
The signals we watch.
Uptime alone does not tell you an AI system is working. We instrument five signals so a problem shows up as an alert, not as a customer complaint.
Operational health
Latency (p50/p95/p99), throughput, error rate, uptime and saturation — the signals that tell you the service is up and fast.
Data & input drift
Distribution shift in the inputs a model sees versus what it was trained on — the quiet cause of silent accuracy decay.
Model quality
Accuracy, groundedness, and task success measured against a baseline on live traffic — is the model still right, not just still running.
Cost & usage
Tokens, calls, GPU hours, and spend per outcome — unit economics tracked against budget so cost never surprises you.
Safety & guardrails
Guardrail hits, policy violations, and blocked outputs — a live view of how often the system is being steered back inside the lines.
When something breaks.
Incidents are inevitable; unmanaged incidents are not. We run a defined response — detect, triage by severity, contain with a rollback or fallback, diagnose, fix, and learn.
Operations, run as a discipline.
The services that keep deployed AI fast, accurate, and affordable — run by us, or built into your team.
MLOps & LLMOps platform
We stand up the pipelines, registries, and environments that turn model work into a repeatable, governed release process.
Monitoring & observability
We instrument metrics, logs, traces, and quality evals into one pane, with alerting that pages the right team fast.
Drift detection & retraining
We watch inputs and outputs for drift and run governed retraining so models recover before accuracy slips.
Inference & cost optimization
We tune serving, caching, batching, and routing to hold latency and slash cost per call.
Reliability & incident management
We define SLOs, build runbooks, and run incident response so failures are contained and learned from.
Serving & deployment automation
We automate canary, shadow, and blue-green releases with rollback, so shipping is safe and boring.
Make inference fast and cheap.
Once a model is serving traffic, cost and latency become engineering problems with known levers. We pull the right ones for your workload — holding quality while cutting the bill and the wait — whether the model runs on a managed provider or self-hosted in your cloud.
Quantization
Run models in FP8 or INT4 to cut memory and cost with minimal quality loss — often the single biggest lever.
LoRA / multi-LoRA serving
Serve many fine-tuned adapters on one shared base model, so specialization costs almost nothing at inference time.
Continuous batching
Pack incoming requests together to keep the GPU busy and push far more throughput per dollar.
KV & prompt caching
Reuse computation across calls and repeated prompts — you pay once for work you would otherwise redo.
Speculative decoding
A small draft model proposes tokens the large model verifies — real latency wins at the same quality.
Right-size & autoscale
Match hardware to the model and the load, and scale to zero when idle so you never pay for silence.
Model routing
Send easy queries to a small, cheap model and hard ones to the big one — quality where it matters, savings where it does not.
Distillation
Compress a big model into a smaller one for the hot path, keeping most of the quality at a fraction of the cost.
Questions teams ask.
Bring us a hypothesis. Leave with a system.
Tell us what's eating your team's time. We'll give you an honest read on whether AI is the right tool — and if it is, a scoped v1 with a timeline and cost.