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

The solution achieved ~60x faster analysis (from hours to seconds), resulted in a patent filing for the novel methodology, and was deployed on-premise for secure data handling.

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. We evaluated 10 open-source parsers across a corpus of 273 business documents. Performance varies substantially by document type—parsers that excel on text-heavy legal documents often struggle with complex layouts. There is no universal parser; we test against your specific data to select the tool that yields highest accuracy.

Agent Architecture: Minimizing Complexity

'Agentic AI' is effective for open-ended tasks, but many business workflows are better served by simpler architectures. In our Customer Triage test case (250 support tickets), 72% of volume could be correctly routed using keyword-based rules before requiring an LLM. We prioritize deterministic code where possible, introducing AI agents only when task complexity requires them.

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.

Get in Touch