Engineering™ · AI & Automation

Agentic Process Automation

We turn complex manual processes into autonomous systems capable of interpreting context, deciding, and executing.

Full business process redesign and automation powered by autonomous AI agents. We do not just pass data between APIs: agents comprehend messy documents, validate business policies, and coordinate actions across disparate enterprise platforms.

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Scope

End-to-End Process Redesign · Multi-Agent Coordination · Unstructured Document Reasoning · Human-in-the-Loop Safeguards

Estimated Timeline

6–10 weeks

Platforms

LangGraph / CrewAI · OpenAI / Claude / DeepSeek · n8n / Temporal · Vector DBs

Target Fit

Is this for your company?

This service is for you if

✓Operations stall at tasks requiring humans to read PDFs, lengthy emails, or unstructured attachments.
✓Qualified professionals spend hours making routine operational decisions based on known policy manuals.
✓Traditional API automations fail because inbound data formats are inconsistent or unpredictable.
✓You need to scale transaction capacity without expanding back-office administrative headcount.

You probably do not need it if

✕Your process is purely mechanical and all inputs already arrive as clean, structured JSON (standard Process Automation is sufficient).
✕Your decision criteria are undocumented or rely purely on subjective intuition.

Problem Space

What we solve

Unstructured Document Reasoning

Extracting and cross-referencing data from complex contracts, scanned receipts, bank slips, and unstructured emails.

Policy-Based Operational Decision Making

Autonomous agents that evaluate business policies, check credit limits, and authorize workflows with audit trails.

Specialized Multi-Agent Orchestration

Agent teams where one agent extracts data, another audits discrepancies, and a third updates the core ERP.

Human-in-the-Loop Safeguards

Automated escalation to human reviewers whenever agent confidence scores fall below defined safety thresholds.

Engineering Process

How it works

01 — Diagnose

Cognitive Process Decomposition

We break down the operational journey into atomic decision nodes, required information sources, and policy guardrails.

02 — Design

Agentic Architecture & Tool Schemas

We design multi-agent topologies, function-calling schemas, confidence thresholds, and review routing.

03 — Build

Engineering, Integration & Benchmarking

We implement agents with LangGraph/n8n, hook up operational tools, and test against hundreds of historical edge cases.

04 — Launch

Assisted Rollout & Production Supervised Run

We run the system in copilot mode alongside human teams, gradually expanding autonomous execution thresholds.

Deliverables

What we deliver

Upon completion you will have

✓Production-grade APA system orchestrating autonomous agents with operational tool capabilities.

✓Document understanding pipeline extracting unstructured data into normalized schemas.

✓Human-in-the-Loop exception review dashboard for human review of edge cases.

✓Automated precision testing suite calibrated against real historical business cases.

✓Secure, encrypted API connections to core ERP, CRM, and financial databases.

Delivery Plan

Implementation Phases

Tiempo típico de proyecto:6–10 weeks

Weeks 1–2Phase 1

Cognitive Audit & Policy Mapping

Case sample gathering, exception documentation, and confidence threshold definition.

Weeks 3–6Phase 2

Agent Engineering & Tool Calling

Agentic logic coding, API tool integration, and precision testing on real historical documents.

Weeks 7–8Phase 3

Human-in-the-Loop Review Dashboard

Interface development for human exception handling and decision audit logging.

Weeks 9–10Phase 4

Supervised Pilot & Production Scaling

Parallel operation with human staff, prompt fine-tuning, and progressive autonomy expansion.

Pricing Guidance

Estimated Investment

Target Investment

USD 6,800

Includes prompt engineering, multi-agent architecture, Human-in-the-Loop interface, and automated evaluation suite.

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Real-World Proof

Impact Case Study

From 4-Day Invoice Processing to 90-Second Autonomous Settlement

Initial problem

Logistics operator with 12 analysts manually reviewing 5,000 carrier invoices monthly in varied formats, suffering calculation errors and 4-day payment lags.

Technical intervention

Deployment of an APA multi-agent system extracting PDF data, reconciling line items against purchase orders, and authorizing ERP payouts.

Outcome achieved

88% of invoices are now settled autonomously in under 90 seconds, freeing analysts to focus solely on the 12% with genuine billing discrepancies.

Clarifications

Frequently asked questions

What prevents the agent from making an erroneous decision?

We implement mathematical confidence scoring and strict guardrails: if the agent cannot verify a data point with complete certainty, the transaction is automatically routed to human reviewers, preventing unauthorized execution.

Are our sensitive business data used to train public models?

No. We exclusively utilize enterprise APIs with strict Zero Data Retention (ZDR) agreements ensuring your corporate documents and queries are never logged or used to train third-party models.

What level of automation is realistically achievable?

Most production implementations achieve between 75% and 90% full autonomous completion, leaving only rare, out-of-policy exceptions for human staff.

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