AI & Machine Learning · CORTEX
AI-powered intelligent automation for finance, HR, and back-office operations: document understanding, decisioning agents, and process orchestration that compound across the operating model rather than bolting RPA onto a broken process.
The problem
The pattern: an automation team picks a vendor (UiPath, Power Automate, Blue Prism), automates 30 of the most-rules-driven processes, claims a productivity dial-up, and then stalls. Documents with variance, exceptions that need judgment, decisions that need context: none of it gets touched. Three years in, the program has 80 brittle bots, an attended-RPA workforce that can't scale, and a CFO asking why the next $5M of automation can't get past pilot.
Modern intelligent automation crosses the line where the work needs *judgment*, and that's where document understanding (LLMs + structured extraction), agentic orchestration (tool use + plan-and-execute), and policy decisioning (programmatic + auditable) replace pattern-matching scripts. We design intelligent-automation programs around the work that judgment unlocks, not the work that scripts already cover.
Where it ships
Specific applications we’ve built and operated. Not speculative: every example below is grounded in a real shipped engagement.
+72%
straight-through processing
LLM-powered extraction with structured outputs, validation against ground truth, and human-in-the-loop checkpoints where confidence falls below threshold. Replaces template-OCR pipelines that miss anything off-template.
−9 days
month-end close
Month-end close acceleration, invoice-to-cash automation, expense reconciliation, and accruals. Combines document understanding with policy decisioning and integrated journal posting.
Onboarding workflows, contract generation, benefits enrollment, and offboarding, with audit trails the compliance team accepts and the IDP / HRIS integrations done as primary scope.
Decisioning agents that route work between human and bot queues based on confidence, complexity, and SLA. Replaces brittle attended-RPA with explainable orchestration.
Inbound case classification, context enrichment from CRM and knowledge base, draft response generation, and escalation routing. Integrates with Salesforce / D365 / ServiceNow case management.
PO matching, vendor onboarding, contract analysis, and spend categorization. Designed for buyers who want to compress procurement cycle time without losing controls.
How we engage
Each phase has a deliverable, an owner, and an acceptance criterion. Not slogans, operating rules.
Two-week discovery: we walk every candidate process and explicitly mark what's deterministic (script work), what needs judgment (AI work), and what shouldn't be automated at all (process redesign first). Output is a prioritized roadmap with realistic ROI per use case.
Six-week pilot on a single high-value process with a real document corpus, real exceptions, and real users in the loop. The pilot proves accuracy, latency, and economics before we scale, not after.
Confidence thresholds, exception routing, audit trails, drift monitoring, and compliance evidence. The platform piece: model lifecycle, prompt versioning, policy decisioning, designed for the regulator, not just the buyer.
Weekly accuracy reviews, monthly drift checks, quarterly platform extensions. Programs compound when they have a continuous owner; they regress when they're treated as a one-time project.
Capabilities
Stack
Selected work
Common questions
RPA automates pattern-following work: it's a recorder for clicks and keystrokes. Intelligent automation crosses the line where the work needs judgment (document variance, exceptions, decisions). The two are complementary: we use RPA-style runtimes for deterministic glue and AI for the judgment-bearing steps. Programs that try to do everything with RPA stall around 30 processes; programs that do both compound past 200.
Accuracy depends on the document class, the corpus quality, and the validation frame. We design pipelines with confidence thresholds: high-confidence outputs route straight through, lower-confidence outputs route to a human queue. On invoice processing we typically land 85–95% straight-through after eight weeks of tuning; on more variable documents (claims, contracts) we expect 70–85% with the rest exception-routed.
Every intelligent-automation pipeline emits a structured event log: input fingerprint, model version, prompt version, decisions made, confidence scores, human reviews, and final action. The log is the audit trail. We've stood up the evidence frame for SOX, HIPAA, and PCI-DSS estates: auditors care about traceability, not whether AI is involved.
Depends on existing investment and the work type. UiPath wins where the existing estate is heavy in attended bots and IT controls the runtime. Power Automate wins where Microsoft 365 is the system-of-engagement layer. Custom (Temporal + your runtime of choice) wins when the work is high-volume, mission-critical, and the runtime needs to evolve with engineering velocity, not a vendor's roadmap.
Process discovery: 2–4 weeks, $40K–$80K. Single-process pilot: 6 weeks, $80K–$200K. Production rollout for a portfolio of 5–10 processes: 6–9 months, $400K–$1.5M. Managed services post-launch: $20K–$60K monthly retainer. Brackets published honestly so prospects self-qualify before the first call.
Yes, and this is the most common engagement shape. We staff the AI-judgment side, your team owns the existing automation estate, and we co-design the orchestration boundary. Hand-off is a runbook plus a 90-day shadow period.
Within AI & Machine Learning
Talk to us
A senior engineer plus the CORTEX department lead joins the first call. No discovery gauntlet, no junior reps.