We build agents that retrieve approved company context, take controlled actions in your systems, and escalate to a human when judgment is required, with full audit trails.
Retrieval over approved documents, policies, and systems, grounded, cited, and current.
Controlled writes to CRM, ticketing, and finance tools within strict permission boundaries.
When judgment is needed, the agent hands off to a named human with the full thread.
Every agent we ship is designed around approved data sources, tool access scopes, human-review gates, and an audit trail your leadership can defend.
Retrieve → reason → act → escalate, with audit at every step.
Flag variances, extract fields, and route exceptions to finance.
Answer repetitive questions from approved docs with citations.
Qualify, enrich, and route work with human review where it matters.
Define the job, the data it can touch, the actions it can take, and where a human must approve.
Wire the agent to your systems with retrieval, tools, and connectors, grounded in your data.
Test against real cases with quality gates and review, so it behaves correctly before it ships.
Launch with logging, cost controls, and monitoring, reliability owned after go-live.
Yes. We deploy private, governed agents with explicit data boundaries and data-loss-prevention controls. Your data isn't used to train public models.
Real work with guardrails, triage and answer tickets, draft responses, pull and route data between systems, generate documents and reports, with a human in the loop wherever approval is required.
We're model-agnostic and use the best model per task, OpenAI, Anthropic, Microsoft, Google, or open models, so you're never locked in.
A first scoped agent is often live in a few weeks; timelines depend mainly on the integrations and approvals involved.
We scope a first agent around one high-value workflow, prove it, then expand with governance intact.
Infonaligy