01 / Input
Understand your environment
Discovery: inputs, target users, allowed data, measurable success criteria and a non-AI baseline.
Services / AI & Automation
Applied AI & Machine Learning Engineering
We integrate models into existing products through typed APIs, permission-scoped tools, retrieval boundaries and evaluation pipelines. We measure quality, latency, data exposure and unit cost against a defined non-model baseline.
Integration architecture
A prototype produces plausible output but has no defined accuracy, latency or failure criteria.
Applied AI and ML engineering for teams incorporating language models, classifiers, embeddings or decision-support workflows into real software. We work from data suitability and business rules through API integration, evaluation and observable operation. Custom model training, fine-tuning or self-hosted inference is scoped only when justified by data, staffing and costs.
Discovery: inputs, target users, allowed data, measurable success criteria and a non-AI baseline.
Scope an AI/ML projectPRODUCTION ACCEPTANCE
A scoped architecture and explicit decision on whether AI is the right solution.
Acceptance and live access are confirmed for the specific customer environment.
Integration path
Integration and verification
Engineering capabilities
- Discovery: inputs, target users, allowed data, measurable success criteria and a non-AI baseline.
- Model and provider selection against accuracy, latency, unit economics, deployment and retention constraints.
- REST/JSON or SDK integration into mobile, web and backend products, with server-side credential isolation.
- Retrieval-augmented generation where justified: ingestion, tenant-aware indexing, citations and data-access policies.
- Structured outputs, schema validation, constrained prompts and deterministic post-processing.
- Offline evaluations with representative consented or synthetic cases, negative tests and regression datasets.
- Observability for response quality, cost, timeouts, provider outages, fallback and human review paths.
- Deployment, rollout flags, documentation, troubleshooting and knowledge transfer to the customer team.
Integration and delivery
- A scoped architecture and explicit decision on whether AI is the right solution.
- Working integration code, API contracts and data-flow documentation.
- Evaluation cases and baseline acceptance thresholds agreed before launch.
- Security, privacy, cost and failure-mode review with monitored rollout controls.
- Deployment checklist, operational runbook and engineering handoff.
Interactive demonstration
Engineering in practice
Interactive engineering guide
Interactive walkthrough of engineering choices and their tradeoffs.
Explore ProjectsHOW THE WORK IS DELIVERED
Expertise you can put to work.
Explore how our engineering and security work moves from discovery to implementation, testing and delivery.
02 / Verification
Investigate and implement
Model and provider selection against accuracy, latency, unit economics, deployment and retention constraints.
03 / Output
Deliver practical results
A scoped architecture and explicit decision on whether AI is the right solution.
Example deliverable structure
Clear findings. Practical next steps.
01Affected component & scope
02Observed behavior and reproduction
03Impact, preconditions and limits
04Correction, owner and retest
Our Approach
How we work with your team
Discovery
We review your goals, existing environment and the systems or workflows involved.
Engagement planning
Data access, model behavior, tool permissions and human approvals are defined before a live integration. We define the delivery plan and responsibilities together.
Results and handoff
Your team receives the agreed implementation or findings, testing results and a clear next-step plan.
Learn more about our process: Our Approach.
Related Services
Next step
Need AI that works inside an actual product?
Share the user workflow, existing stack, available data, privacy requirements and what a successful result must look like. We can define an implementation and evaluation plan.