MLOps + LLMOps01 / 06
MLOps + LLMOps
Training pipelines, model and prompt registries, canary deploys and shadow traffic, instant rollback when the eval line crosses.
AI & ML Operations
Eval is the product.
The model is the easy part.
MLOps and LLMOps, computer vision, RAG and agentic systems — engineered around the model: the pipelines that feed it, the harnesses that catch it failing, and the deployment surface that lets it ship.
AI & ML Operations
- MLOps + LLMOps
- Agents, RAG + chatbots
- Computer vision + VLM
What we ship
Productionised, not demoed.
We integrate foundation models into production systems — we don’t train them. Benchmarks pick the starting model; your data decides if it ships.
02 / 06
Agentic systems + chatbots
RAG that works at scale, tool use you can audit, token budgets enforced, guardrails that survive prompt injection.
03 / 06
Computer vision + VLM
Defect detection, claims OCR and ID verification on messy real-world data, with ViT, SAM, CLIP, LLaVA and Florence-2.
04 / 06
Evaluation harness
Task-specific held-out evals on your own data, segmented by cohort and edge case, refreshed continuously.
05 / 06
Drift + calibration
Drift detectors that fire before the business metric does; calibration checked at every retrain. Confidence has to be true.
06 / 06
Gated rollout
Shadow, canary, then promotion — each stage with a pre-registered metric, a hard guardrail and an automatic rollback.
How we work
Filed publicly, so partners know what to expect.
Answer rate is the SLA: a 24-hour reply on every inbound. Not “next sprint.” Not “after discovery.”
- 47
- Models in production
- 99.7%
- Inference uptime · 2025
- 200ms
- p99 latency · production
- 01
We engineer; we don’t deck.
Work shows up as code, infrastructure and shipped systems. Slides arrive late, if at all.
- 02
One SOW. One team.
No subcontractor cascade. The engineers you meet on day one write the code on day ninety.
- 03
We ship our own products first.
Every Labs product was paid for by us before it was sold. We don’t pitch what we haven’t bet on.
- 04
We say so when we’re not the right fit.
If your problem belongs elsewhere — Big-4, offshore, your own team — you’ll hear it on the first call.
Engagement · declassified sample
“Insurance — computer-vision claims pipeline. Document classification accuracy from a 76% baseline to 98.4% with calibrated abstain; manual review queue cut by 71%.”
Client identity protected by NDA. Named references available on request.
Related capabilities
The same engineers behind every capability.
Each capability is staffed by the same studio — not siloed teams handing work over walls.
01
Data & Intelligence
Enterprise data and AI, shipped as intelligence.
02
Security
Engineered defense for the systems we build.
03
Engineering
End-to-end software for systems that must not fail.
Open a file
Bring the problem.
We bring the senior practitioner.