Technology
Technical capabilities
The models, frameworks, and platforms we build with, and the engineering practices that make agents dependable.
The technologies below describe our engineering capabilities and tools we may use on a project. They do not imply partnerships, certifications, or endorsements by the vendors named. We select tools to match each client’s requirements and existing stack.
AI Models
- OpenAI
- Anthropic
- Open-source LLMs
Agent Engineering
- LangGraph
- OpenAI Agents SDK
Backend
- Python
- FastAPI
- Node.js
- Spring Boot
Frontend
- React
- Next.js
- TypeScript
- Angular
Data
- PostgreSQL
- MongoDB
- Vector search
- Redis
Infrastructure
- AWS
- Azure
- Docker
Enterprise Integrations
- REST APIs
- CRM
- ERP
- Third-party services
Reliability & Governance
Engineering practices behind production agents
An agent that works in a demo is not yet an agent you can trust with real work. These practices close that gap.
Agent evaluations
Fixed test sets and regression checks run before each release, combining automated scoring with human review of sampled outputs.
Observability
Traces for model calls, tool calls, and agent decisions, with cost, latency, and error metrics.
Security
Secrets management, input and output validation, prompt-injection awareness, and least-privilege tool access.
Human-in-the-loop workflows
Approval and review steps for high-impact actions, with clear escalation paths from the agent.
Access controls
Role-based permissions and service identities so agents see and change only what they are allowed to.
Audit logging
Immutable records of inputs, decisions, and actions to support review and accountability.
Questions about your stack?
Tell us what you already run and we will suggest how an agent could fit in.
Start a Conversation