AI Automation Systems
For work that moves between people, documents, business rules, and AI. Each step is explicit, observable, and reviewable.
Typical result: a controlled workflow with structured inputs, decision points, human review, and recorded outputs.
- Workflow and approval orchestration
- Scheduled and background operations
- Structured AI outputs and validation
- Audit history, escalation, and review controls
Document Intelligence
Turn PDFs, scans, statements, invoices, and forms into validated records that downstream systems can use.
Typical result: an intake pipeline with retrieval, OCR, extraction, quality checks, exceptions, and reviewed delivery.
- PDF, scan, invoice, and form extraction
- OCR and document classification
- Schema validation and exception queues
- Human review and structured exports
Browser Automation
Persistent, authenticated browser workflows for systems that do not provide a suitable API.
Typical result: an operated browser process with durable sessions, validation, retries, and manual recovery controls.
- Authenticated portal workflows
- Report retrieval and file transfer
- Reviewed form workflow automation
- Monitoring, retries, and run history
Custom AI Applications
Task-specific applications that connect approved models to documents, databases, APIs, and internal tools.
Typical result: a private team application with scoped retrieval, tool permissions, structured responses, and review paths.
- Hosted or local model architecture
- RAG, MCP, and tool integrations
- Knowledge search and team workspaces
- Access controls, evaluation, and review
Backends & Internal Tools
Business applications that combine APIs, relational data, authentication, administration, reporting, and integrations.
Typical result: a secure backend and responsive operational interface matched to real roles and processes.
- FastAPI or Django services
- Authentication and role-based access
- CRM, dashboards, and administrative tools
- API integrations and business rules
Data Pipelines
Reliable ingestion, normalization, synchronization, and reporting across APIs, files, databases, and scheduled sources.
Typical result: repeatable data movement with deduplication, validation, failure handling, and traceable runs.
- API, file, and database ingestion
- Cleaning, mapping, and deduplication
- Scheduled jobs and source synchronization
- Run history, validation, and reporting
Cloud & Deployment
Deployment and operation across AWS, Google Cloud, Azure, containers, Linux virtual machines, and edge services.
Typical result: a documented production environment with repeatable releases, monitoring, alerts, backups, and recovery steps.
- AWS, Google Cloud, and Azure
- Docker, Linux, reverse proxies, and TLS
- CI/CD and infrastructure automation
- Logs, metrics, alerts, backups, and runbooks