AI &
AUTOMATION.
We engineer AI applications, agents, knowledge systems, document workflows, and automation connected to real software, data, and business processes.
AGENTS
Tool-Using Workflows
RAG
Knowledge Retrieval
DOCUMENT AI
Data Extraction
AUTOMATION
Connected Systems
WHAT WE BUILD.
AI Applications & Agents
We build AI features and agent workflows as software systems that combine models, tools, APIs, and application state. Multi-step execution and human approval points are designed around product requirements.
- AI application development
- Tool & API integration
- Multi-step agent workflows
- Human-in-the-loop controls
Knowledge & RAG Systems
We build retrieval systems that use internal documents and data to support semantic search and source-aware responses, with access controls where required.
- Retrieval-Augmented Generation
- Semantic search
- Document indexing
- Source-aware responses
Document AI & Data Extraction
We extract structured data from PDFs, images, invoices, forms, catalogs, and receipts, with validation and review workflows matched to document quality and risk.
- Document extraction
- Structured JSON output
- Validation workflows
- Human review when required
Workflow Automation
We automate workflows with APIs, webhooks, queues, background jobs, and business rules, adding AI for classification, extraction, drafting, routing, or decision support when it is useful.
- APIs, webhooks & queues
- Business rules & background jobs
- AI-assisted workflow steps
- Human approval & escalation
AI TECH STACK.
We choose models, orchestration frameworks, databases, and infrastructure based on product requirements, data, latency, privacy, reliability, and cost. Providers and tools vary by engagement.
# Provider-agnostic retrieval and generation flow
def answer_question(question: str, user: User) -> Answer:
# Retrieve relevant context before generation
documents = retriever.search(
query=question,
filters=access_scope(user),
limit=5,
)
context = build_context(documents)
draft = model.generate(
prompt=build_prompt(question, context),
schema=AnswerWithSources,
)
return validate_response(draft, documents)
FREQUENTLY ASKED QUESTIONS.
How do you reduce incorrect AI answers?
We reduce incorrect answers through techniques such as retrieval, constrained prompts, structured outputs, validation, access controls, evaluations, and human escalation. For high-risk workflows, AI output should be reviewed or validated before an action is performed.
Can you add automation to our existing software and business systems?
Yes. Workflows can respond to webhooks, scheduled events, user actions, or API events. Deterministic steps can use APIs, queues, background jobs, and business rules, while AI can support classification, document processing, drafting, routing, and decision support where appropriate. Human approval can be included when the workflow or risk requires it.
What vector databases and embedding models do you use?
Technology is selected based on dataset size, retrieval requirements, language support, hosting constraints, cost, and the application's existing stack. Options may include PostgreSQL with pgvector, managed vector databases, and embedding APIs from model providers.
How do you approach data privacy when using AI providers?
Data handling depends on the model provider, API plan, enabled features, retention settings, and deployment architecture. We design integrations around the client's privacy and security requirements and review provider data controls before production use. The architecture may include access controls, data minimization, encryption in transit, appropriate retention configuration, separation of sensitive data, or private infrastructure where requirements justify it.
LET'S BUILD WITH AI.
Tell us what you're trying to automate or build. We'll help determine where AI, deterministic software, or a combination makes sense.
Schedule a Discovery Call