Real architectures built inside real enterprises. Measured in hours recovered, errors eliminated, and people redirected to work that actually requires their expertise.
A multinational logistics company operating across 30+ countries had teams of compliance specialists manually cross-referencing complex bills of lading against constantly changing international regulatory files. Each shipment required a human to pull the bill of lading, look up the current regulations for the destination country, verify every line item against the regulatory requirements, and flag discrepancies. The work was repetitive, high-stakes, and entirely manual. One missed regulatory update could mean shipments held at port for weeks, costing the company $50K+ per incident in delays and penalties.
They had tried RPA tools. The tools worked for standard shipments but broke on non-standard formats, regulatory edge cases, and multi-jurisdiction filings — which represented 35% of their volume and 80% of their compliance risk.
We embedded an engineer on-site for six weeks. They shadowed the compliance team, mapped every document type, every regulatory source, and every exception workflow. Then they built a multi-agent pipeline:
The system was deployed on the company's existing AWS infrastructure behind their firewall. Zero data leaves their environment.
A mid-market fintech processing $200M+ in annual loan volume was receiving thousands of applications daily. Each required a human analyst to manually review bank statements (PDF, CSV, and screenshot formats), tax returns (multiple years, multiple formats), pay stubs, and identity documents. The analyst had to extract financial data from each document, cross-reference it across sources, enter it into their risk scoring system, run the assessment, and make a recommendation.
Each application took 45–90 minutes of analyst time. The backlog was growing faster than they could hire. They were turning away qualified borrowers because they couldn't process applications fast enough, and their customer NPS was dropping because applicants waited days for decisions their competitors delivered in hours.
Our forward-deployed engineer spent the first two weeks embedded with the underwriting team, reviewing hundreds of actual applications, catalogued every document format, mapped every decision rule, and identified the specific points where manual work was creating bottlenecks versus adding genuine judgment.
A regional property and casualty insurer processing 2,000+ claims monthly had a claims lifecycle that touched six departments: intake, document collection, damage assessment, coverage verification, adjuster assignment, and payment authorization. Each handoff introduced delays, data re-entry, and opportunities for errors. Standard auto claims that should have been routine were taking 14–21 days to resolve because of the sheer number of manual steps and inter-department coordination.
Claimants were frustrated. Adjusters were spending 70% of their time on administrative tasks instead of complex loss assessment. And the company was losing customers to competitors with faster resolution times.
We embedded an engineer who mapped the entire claims lifecycle end-to-end, identifying which steps required genuine human judgment (complex damage assessment, fraud investigation, coverage disputes) and which were purely mechanical (document collection, coverage lookup, standard damage estimation, payment routing).
A regional hospital network with 12 facilities was processing 800+ prior authorization requests weekly. Each request required staff to read the physician's order, look up the payer's specific authorization requirements (which vary by insurer, plan type, and procedure), gather supporting clinical documentation from the EHR, compile the submission package, and submit it through the payer's portal — often manually, because each payer has a different portal and format. The average turnaround was 5 business days, and denials due to incomplete submissions were running at 23%.
A team of smart people doing repetitive, rule-driven work that's too complex for off-the-shelf automation but too mechanical for the talent you've hired. Documents that need to be read and understood. Data that needs to move between systems with validation at every step. Decisions that follow clear criteria 85% of the time but require human judgment for the rest.
We embed an engineer, build custom AI workflows against your actual systems using the best available models and orchestration frameworks, and redirect those people to work that creates real value — the complex cases, the strategic decisions, the customer relationships that require a human mind. The math is always obvious in hindsight. The only question is how long you wait before you do it.
Tell us about the workflow. We'll tell you what we'd build, what technology we'd use, and what kind of results to expect.
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