AI in production. Not demos.
Model design, training pipelines, evaluation, and inference at scale. AI features engineered to survive contact with real users, real latency budgets, and real cost ceilings. Outcomes measured against the workflow KPI. Not the demo.
Built to compound.
Every engagement starts with the workflow KPI. If we cannot draw a line from the model to the number the business runs on, we do not build it.
AI initiatives fail in production. Not because the models are weak — but because the systems around them were never engineered as production systems. We deliver AI and machine learning as a discipline. Use-case shaping. Data and training pipelines. Inference at scale. Evals. Cost and safety guardrails. Ongoing care.
Predictive Analytics and ML
Forecasting, churn, propensity, anomaly detection, computer vision, NLP. Production-grade models tied to the workflow KPI — not lab demos. Evals and retraining cadence built in.
Outcomes, Not Activity
AI features shipped against real users, real latency targets, and real cost envelopes. Outcomes measured, not promised. Improvements proved before they are claimed.
Certified Across Google, AWS, and Azure AI
Deep credentials across the major cloud AI platforms. We pick the stack that fits your latency, cost, and compliance envelope — not the one we used last.
Eval-Driven Development
Offline eval harness. Online metrics. Prompt and policy regression suites. Quality drift is caught before users see it — not after.
Cost-Aware Inference
Caching, batching, model routing, quota guardrails, open-model fallback. Token spend is a first-class engineering constraint — not a quarterly surprise on the bill.
Owned End-to-End
Use-case shaping through ongoing operation — one team, one shared backlog, one accountable engineer from week 1 to day 91 and beyond.
AI is a production system. We deliver it that way — with the same engineering discipline as the rest of the platform.
What you walk away with.
Concrete deliverables — everything is yours at the end of the engagement.
Use-case shaping
Problem framing · success metrics · build-or-buy call per component.
Data and training pipeline
Feature store · training infrastructure · reproducible runs · eval harness.
Inference stack
Serving · caching · cost guardrails · A/B and shadow rollouts.
Eval and safety
Offline evals · online metrics · prompt and policy regression suite.
AI & Machine Learning delivery, step by step.
A sequenced engagement from first call to production hand-over — transparent, measured, accountable.
Assess
Use cases · data audit · feasibility scoring.
Design
Architecture · model choices · eval criteria.
Build
Training, serving, evals end to end.
Migrate
Shadow then canary then production · cost and quality gates enforced.
Operate
Playbook · on-call · ongoing eval cadence.
Pricing
Starter
Perfect for small businesses getting started with the cloud.
- Cloud readiness assessment
- Up to 5 workloads migrated
- Business-hours support
- Monthly cost review
Professional
For growing teams that need more power and automation.
- Everything in Starter
- Up to 25 workloads migrated
- CI/CD pipeline automation
- 24/5 priority support
- Quarterly architecture review
Enterprise
Tailored solutions for large-scale, mission-critical deployments.
- Unlimited workloads
- Dedicated senior engineer
- 24/7 mission-critical support
- Custom SLAs & compliance
- On-site engagement options
AI & Machine Learning in the field.
Posts, trends, and client stories tied to AI & Machine Learning.
Tell us what you need.
Drop a line — a senior engineer replies the same business day. No SDRs, no decks.
Get in touch
Talk to us about AI & Machine Learning.
A senior engineer replies the same business day. No SDRs, no decks.