Applied AI products-generative, predictive, and evaluative-ready for production.
AI & Machine Learning
Design, build, and operate AI features with robust data pipelines, eval harnesses, and safety guardrails.
Prototype to prod
4-6 weeks
LLM + retrieval with eval loops
Quality
Live evals & guardrails
toxicity, PII, hallucination checks
Ops
Automated drift alerts
Data + model monitoring
What you get
Outcomes we anchor every engagement to.
Clear measures of success up front-so we design workstreams, checkpoints, and KPIs that prove value early and often.
Grounded answers
Retrieval, citations, and scoring keep outputs trustworthy.
Reliable pipelines
Versioned data, feature stores, and CI for models.
Governed AI
PII handling, safety filters, and audit trails by default.
Service modules
Mix-and-match modules to fit your goals.
Each module includes concrete deliverables and owners. We start with the smallest set that proves value, then scale.
GenAI products
- Retrieval-augmented chat/agents with citations
- Document understanding, summarization, redaction
- Workflow copilots integrated with internal tools
- Evaluation harnesses with human + automated scoring
ML systems
- Prediction services (ranking, forecasting, scoring)
- Feature store design and data contracts
- Model registry, CI for models, and deployment automation
- Canary + shadow deployments with rollback
Data & governance
- Data quality checks and lineage
- Safety guardrails (PII filters, jailbreak tests)
- Cost/performance optimization across providers
- Playbooks for human-in-the-loop review
Delivery playbook
How we run the work day to day.
Transparent cadence, artifacts you can keep, and checkpoints that keep stakeholders aligned without slowing velocity.
AI readiness audit
Assess data, risks, and the right model/provider fit.
- Use-case + risk canvas
- Data availability + gaps
- Guardrail plan + KPIs
Prototype & eval
Ship a working slice with evals before scaling.
- Prompt + retrieval design
- Automated eval suite (quality, safety)
- Human review loop
Productionize
Operationalize with monitoring, governance, and cost controls.
- Model registry + versioning
- Canary/shadow deploy
- Drift, cost, and latency dashboards
Engagement models
Choose the shape that matches your stage.
Time-boxed sprints for validation, squads for ownership, or retainers for steady improvements.
AI discovery + pilot
Validating an AI use case with stakeholders
- Pilot shipped to prod or secure staging
- Eval + safety harness
- Rollout + adoption plan
Productized AI
Owning an AI feature end-to-end
- Data/feature pipelines
- Model ops + monitoring
- UX + change management
Model lifecycle support
Teams that need continuous tuning
- Evals + guardrails upkeep
- Retraining + cost tuning
- Incident response for AI outputs
Sample timeline
How the first weeks typically unfold.
We tailor depth and duration to the scope, but every phase ends with tangible artifacts you can use.
Week 1
Objectives
- Use-case + risk workshop
- Data audit and success metrics
Artifacts
- Canvas + KPI targets
- Annotated sample data
Weeks 2-3
Objectives
- Retrieval/prompt design
- Initial eval suite + guardrails
Artifacts
- Pilot deployed to staging
- Eval dashboards
Weeks 4-6
Objectives
- Pipeline hardening
- Monitoring + alerts
- Shadow or canary go-live
Artifacts
- Model registry entries
- Runbook + rollback
- Cost + latency budgets
Post-launch
Objectives
- Collect feedback & retrain
- A/B and quality reviews
Artifacts
- Evals + drift reports
- Iteration backlog
Tools & accelerators
Stacks and accelerators we bring.
We stay tool-agnostic but opinionated. These are our defaults; we adapt to your standards and vendors.
Use cases
Where this service fits best.
- Knowledge assistants with grounded answers
- Document intake: classify, extract, and summarize
- Forecasting or scoring models with live monitoring
- Content safety and PII redaction pipelines
- Copilot-style workflows embedded in internal tools
FAQs
Details teams usually ask us about.
Do you only use one model provider?
No. We design for provider choice-OpenAI, Anthropic, Gemini, or local models-based on latency, cost, and data policies.
How do you measure quality?
We create automated evals (accuracy, safety, latency), plus human review sampling tied to acceptance thresholds.
Can you work with our data team?
Yes. We align on schemas, governance, and infra so data contracts and pipelines fit your existing stack.