Intelligent Workflow Automation

We build hybrid systems that combine deterministic logic with probabilistic AI to process unstructured data reliably.

The Reality of Unstructured Data

Automating workflows involving documents—contracts, forms, and correspondence—requires more than a Large Language Model. It requires a pipeline that handles formatting inconsistencies, varying document quality, and specific business rules.

Effective automation isn't about forcing AI into every step. It's about architecting the right flow of data. We combine standard software engineering (for stability and cost-control) with targeted AI (for reasoning and extraction).

Case Study

Contract Intelligence at Scale

Context

A Fortune 100 global reinsurer needed to extract decision-critical attributes from complex contract templates. The documents weren't simple forms—they contained dense, domain-specific language where accurate extraction directly impacted downstream business processes.

The Solution

We developed a pipeline combining:

  • 1 Specialized document parsing to preserve structural hierarchy
  • 2 Domain-specific embeddings to locate relevant clauses
  • 3 Multi-stage verification to ensure extraction accuracy

The Outcome

~60x
Faster analysis (hours to seconds)
Patent Filed
Novel methodology
On-Premise
Secure deployment

Technical Approach: Evidence-Based Engineering

We make architectural decisions based on testing and measurement, not assumptions.

Document Processing: The "Right Tool" Approach

Extracting data from PDFs is non-trivial, and performance varies significantly by document type.

  • The Benchmark: We evaluated 10 open-source parsers across a corpus of 273 business documents.
  • The Finding: Performance varies substantially by document type. Parsers that excel on text-heavy legal documents often struggle with complex layouts like invoices.
  • The Implication: There is no universal parser. We test against your specific data to select the tool that yields highest accuracy for your document structure.

Agent Architecture: Minimizing Complexity

"Agentic AI" is effective for open-ended tasks, but many business workflows are better served by simpler architectures.

  • The Experiment: We built a Customer Triage workflow using multiple implementation methods to compare performance.
  • The Finding: In this test case (250 support tickets), 72% of volume could be correctly routed using keyword-based rules before requiring an LLM.
  • The Implication: We prioritize deterministic code where possible, introducing AI agents only when task complexity requires them. This keeps costs predictable and behavior transparent.

How We Engage

1

Discovery & Assessment

We review your current workflows to identify where automation is feasible—processes where inputs are consistent enough to be reliable, but complex enough to benefit from AI.

2

End-to-End Implementation

We design and build the full pipeline: data ingestion, logic layer (rules + AI), and integration with your existing systems.

3

Evaluation Framework

We establish measurement against ground truth, ensuring automation delivers expected accuracy and efficiency over time.

Discuss Your Project

Let's explore which of your workflows are ready for intelligent automation.

Schedule a Consultation