Production AI, engineered end to end, six eval-gated service lines.
The same playbook, tuned to the constraints of the sectors we ship into most.
Proof, not promises, selected case studies and recognition.
A transparent, 3-phase playbook from first audit to embedded team.
The senior team behind the work, and how to reach us.
AI that works in a demo and AI that holds up under production load are different systems. We build the infrastructure layer (serving, retrieval, eval pipelines, drift monitoring) that keeps your models dependable, cost-efficient, and audit-ready once real traffic arrives.
Most AI projects fail in production, not in development. We build the infrastructure that closes that gap eval gates, drift alerts, and observability from the first deployment.
Pinecone, Weaviate, Qdrant, and pgvector we design the chunking strategy, embedding pipeline, and hybrid search configuration for your retrieval use-case.
vLLM, TGI, Triton, and managed endpoints. We set up autoscaling inference with P95 latency SLAs, GPU cost optimisation, and blue-green deploys.
CI-integrated eval runs that score every model change before it ships. RAGAS, Deepeval, Braintrust, or custom harnesses run on every pull request.
Statistical drift detection on model outputs and embeddings. Automated alerts when your AI system starts behaving differently than it did at launch.
Token cost tracking, latency profiling, prompt/response logging, and audit trails. Full observability across every LLM call in production.
ETL and streaming pipelines that keep your vector stores and fine-tuning datasets fresh. Airflow, Prefect, or custom Python pipelines on your infrastructure.
LLM outputs degrade gradually no error thrown, no alert fired. Users leave before you notice. Drift monitoring catches this before it becomes a churn event.
Without eval pipelines, every model update is a gamble. You can't know if the new version is better or worse on your actual production queries.
Unmonitored LLM usage scales cost unpredictably. Token tracking and cost alerts keep bills predictable as usage grows.
Regulated industries require proof of what your AI said, when, and why. Retroactively reconstructing that is expensive. Logging it from launch is cheap.
Yes, this is our most common MLOps engagement. We start with an infrastructure audit, then add eval pipelines, drift monitoring, and observability without disrupting your existing deployment.
It depends on your scale, query patterns, and whether you need metadata filtering or hybrid search. We run a bake-off on your data before recommending. Pinecone for managed scale, pgvector for PostgreSQL shops, Qdrant for self-hosted flexibility.
We set up automated eval runs that compare a random sample of live inferences against your golden dataset. When accuracy drops below threshold, you get an alert before users start complaining.
Yes. We integrate eval runs into GitHub Actions, GitLab CI, or CircleCI. Model deploys are gated on eval pass a regression blocks the deploy, not the user.
30 minutes, one of our seniors, no slide deck. By the end of the call you'll know whether we're the right team, and if not, who is.