Candidate sourcing & multi-dimensional evaluation pipelines
Agents source and parse candidates, score them across several skill dimensions, and write results straight into the ATS.
Read the case studySolutions / Agentic AI & Workflows
01 / OrchestrationBeyond automation: multi-agent systems engineered for autonomous operations.
Move from rigid RPA bots and isolated prompts to goal-directed AI agent swarms capable of reasoning, validating, and executing complex enterprise tasks with human-in-the-loop governance.
Technical architecture
Four stages from a business goal to an audited action.
Every agentic system we ship follows the same control path, so each action traces back to a goal, an agent, and an approver.
Ingestion & goal decomposition
Illustrative pseudo-code
Featured enterprise use cases
Where agent teams are doing real work.
Autonomous revenue cycle management & medical coding audit
Document agents extract clinical detail, propose ICD-10 codes, and audit each claim before it is submitted.
Read the case studyMulti-pass regulatory compliance verification
Independent agents check the same document against each rule set. When they disagree, the case goes to a reviewer with both findings attached.
Discuss your rule setsFAQ
Questions enterprise teams ask first.
How do you stop an agent from taking an unsafe action?
Agents never write to a system of record directly. Every proposed action passes a deterministic safety gate that validates it against a schema and your business rules. Anything outside those rules goes to a named human approver, and every decision is logged.
How do you prevent hallucinated outputs?
Agents reason over data retrieved from your systems, not over model memory. Outputs are checked against typed schemas, and a separate verification agent cross-checks results before they reach the safety gate.
Is our data used to train public models?
No. We deploy on enterprise Azure OpenAI instances inside your Azure tenancy. Your prompts, documents, and outputs are not used to train public models.
How do you manage latency in multi-step workflows?
Independent agent steps run in parallel, repeated lookups are cached, and routine steps go to smaller, faster models. We measure latency per step in production, so a slow stage is visible and fixable.
Find out which of your workflows an agent team can run.
We map one workflow end to end, identify the safety gates it needs, and estimate the operational throughput you can expect.