We design, train, and operate machine learning and generative AI systems that hold up in production — grounded in your data, measured against your KPIs, and built to evolve with your product.
Who this is for
- Product and engineering teams whose AI prototype now needs to survive real production traffic, edge cases, and audits.
- Companies sitting on unused data — support tickets, transaction logs, sensor streams — that hasn’t been turned into a working model yet.
- Regulated businesses (healthcare, finance) that need AI decisions to be explainable and defensible, not just accurate.
- Teams running a generative AI or agentic feature that needs cost, safety, and reliability guardrails before wider rollout.
Problems we solve
The challenge
A prototype that worked in a notebook breaks under real traffic, edge cases, and drifting data.
Our approach
We rebuild it as a production system: versioned models, automated retraining, and monitoring that catches drift before users do.
The challenge
Leadership can’t tell if the AI initiative is actually moving a business metric, or just a cool demo.
Our approach
We tie every model to a measurable KPI from day one — cost per ticket resolved, time-to-diagnosis, conversion lift — and report against it.
The challenge
Generative AI features feel unpredictable: hallucinations, runaway costs, no audit trail.
Our approach
We add guardrails — bounded autonomy, human escalation paths, per-task cost ceilings, and full action logging — the same patterns from our own field guide on agentic AI in production.
The challenge
Regulated industries need to explain and defend an AI decision, not just make one.
Our approach
We design for explainability and audit trails from the architecture stage, not bolted on after a compliance review flags it.
Capabilities
- Generative & agentic AI solutions
- ML platforms & MLOps
- Computer vision & visual intelligence
- AI strategy & readiness assessment
How we work
- 01
Assess & scope
Audit data readiness, define the KPI the model must move, and size the problem before writing a line of model code.
- 02
Prototype with a production lens
Build the first model against real data slices, not a curated demo set, so failure modes surface early.
- 03
Harden for production (MLOps)
Wrap the model in the pipeline it needs to survive: versioning, retraining, monitoring, guardrails, cost controls.
- 04
Operate & improve
Run it, watch the KPI, retrain on drift, and expand scope once the first use case earns trust.
Technologies we work with
- PyTorch
- TensorFlow
- LangChain
- Hugging Face
- Vertex AI
- AWS SageMaker
- Kubernetes
- MLflow
- Weights & Biases
- Ray
In numbers
Frequently asked questions
Do you only build custom models, or also integrate LLM APIs like OpenAI or Anthropic?
Both — we pick whichever gets you to a defensible result fastest, whether that’s fine-tuning an open model, training from scratch, or wrapping a hosted LLM in the right guardrails.
How do you handle AI project costs that spiral once it’s live?
We set per-task cost ceilings and monitor spend from day one — the same FinOps discipline we apply to cloud platforms.
Can you work with our existing data science team instead of replacing them?
Yes — most engagements embed alongside an in-house team, filling MLOps, infrastructure, or production-hardening gaps rather than taking over the whole roadmap.
What if we only have a rough idea and no clean dataset yet?
We start with an AI readiness assessment — what data you actually have, what it would take to make it usable, and whether the use case justifies the investment before you commit further.
As a software development company, do you also handle the product around the model — not just the AI?
Yes — most AI engagements pair with our Digital Product Engineering team, since a model is only useful once it’s wired into a real interface, workflow, and release pipeline.