Tech services

What we do

We partner with teams to deliver end-to-end AI-powered products, combining product engineering, AI systems, and the foundations required for reliability at scale. From discovery and architecture to implementation and production operations, we help you ship safely, iterate faster, and keep quality measurable over time.

1. AI Products & Agentic Systems

We build copilots, agents, and intelligent automation that integrate into real workflows.

Design. Define the workflow, boundaries, and guardrails: tool access, human-in-the-loop approvals, safety constraints, and failure modes.
Build. Implement agentic workflows and copilot experiences that connect to your APIs and systems, including retrieval/RAG where needed.
Operate. Add tracing, monitoring, and iteration loops so behavior improves safely as prompts, tools, and models evolve.

2. AI Evaluation & Observability

We set up evaluation and production observability so AI behavior is measurable, regressions are caught early, and releases are made with confidence.

Design. Specify what “good” looks like: evaluation criteria, test sets, quality thresholds, and release gates for AI behavior.
Build. Implement evaluation workflows (offline + CI) and observability for production: traces, latency/cost metrics, tool-call analysis, and feedback capture.
Operate. Track changes over time, surface regressions early, and create a repeatable loop to improve quality with confidence.

Built in-house: Aegis. We also built Aegis, our AI evaluation platform, to run the same workflows we deliver for clients: automated evaluation, regression detection, and release confidence for GenAI systems.

3. Product Engineering

We build full-fledged products end-to-end: SaaS platforms, APIs, and internal tools, with AI designed into the system.

Design. Shape architecture and delivery milestones with a pragmatic approach to security, performance, and maintainability.
Build. Deliver the product across the stack: backend services and APIs, web apps and dashboards, integrations, and workflows.
Operate. Ensure it runs well in production with testing, observability, performance tuning, and steady iteration.

4. Data & ML Foundations

We build the data and ML foundations that make AI reliable in production: pipelines, governance, and retrieval-ready architectures.

Design. Define the data model and platform approach: sources, access patterns, governance, quality controls, and scaling needs.
Build. Implement ingestion and transformation pipelines, lakehouse/warehouse foundations, and retrieval/embedding pipelines for AI use cases.
Operate. Keep data trustworthy over time with quality checks, monitoring, lineage, and predictable operational processes.