Business agents, LLM assistants, RAG systems, document processing, and AI-powered workflow automation — integrated into real business processes, not demo environments.
Production-grade software with real business logic, integrations, and engineering maturity behind it.
AI Business Agents — Autonomous agents that execute multi-step business processes — scheduling, approvals, data lookups, notifications, and escalations.
LLM Assistants — Domain-specific chat assistants prompted or fine-tuned on your knowledge base, products, and processes — far beyond generic chat wrappers.
RAG Over Company Documents — Retrieval-augmented generation systems that let your team query contracts, manuals, reports, and knowledge bases in plain language.
AI Document Processing — Extract, classify, validate, and route data from invoices, contracts, forms, and reports — replacing manual data-entry pipelines.
Support & Sales Conversational AI — Assistants that handle tier-1 support, qualify leads, book demos, and escalate intelligently — with clear human-in-the-loop boundaries.
CRM, ERP, Helpdesk & Messenger Integrations — AI workflows connected to Salesforce, HubSpot, SAP, Zendesk, Slack, Teams, and email — so automation lives where your team already works.
We map your processes, identify high-ROI automation candidates, and define success metrics before touching code.
LLM selection, orchestration framework, data sources, integration points, and security model — designed for production.
Short sprints with working demos against real data at every checkpoint.
Connected to your live systems. Load tested, edge-case tested, hallucination-mitigated.
Shipped to production with observability, cost tracking, and alerting for model drift or failures.
We monitor performance, evolve prompts, and adapt the system as your business changes.
Most AI projects fail not because of the model — but because of poor integration, shallow understanding of business context, and lack of engineering maturity in the team building it.
We bring 19+ years of production software delivery to every AI engagement. We know how enterprise systems behave under load, how to handle data governance, and how to build AI that your operations team can actually run.
Book a Discovery CallDiscovery, coding, testing, refactoring, and documentation accelerated by AI-assisted workflows.
Senior engineers own architecture, security, quality, and business logic — no exceptions.
Engineers selected for relevant industry exposure, not generalists learning on your budget.
We build for maintainability, evolvability, and total cost of ownership — not just demo day.
Let's discuss whether it is feasible, what it would take, and what outcomes you could expect. Free, no commitment.
Clear answers about scope, delivery, risk, and engineering control.
An AI agent can classify requests, retrieve knowledge, extract structured data, draft responses, route work, trigger approved actions, and prepare summaries. The exact scope depends on data access, risk, and the business process.
A RAG assistant retrieves approved company information and cites the source used for an answer. A general chatbot relies mainly on model knowledge and is less suitable for controlled business information.
Yes, when architecture, access control, data retention, model selection, and audit logging are designed for the required security level. Sensitive workflows may use private cloud or on-premise components.
High-impact or ambiguous actions should include human approval, confidence thresholds, deterministic rules, and escalation paths. Low-risk repeatable tasks can be automated more fully after evaluation.
Yes. AI agents can connect to CRMs, ERPs, document stores, ticketing systems, email, internal APIs, databases, and workflow tools through a controlled integration layer.