--- title: "Results" description: "Results from our own operations and from customer systems in production, and the patterns we build most." canonical: "https://bespinus.github.io/bgus-ai-landingpage/results/" updated: "2026-10-01" --- # Proof from production. Results from our own operations and from customer systems in production, and the patterns we build most. ## We ran the stack on ourselves first. Before we presented the stack to anyone, we ran it on ourselves. The people who knew the work chose what to automate and designed the agents themselves. We manage our agents the way we manage our people: on results. ### 751 agents designed Built by the teams who do the work, not handed down by a central group. ### 511 in production In production, saving 36,800 hours a year. ### 240 retired They worked. None paid for itself, so we retired them. ### Our own software, rebuilt around agents We rebuilt parts of our own deal process as an agentic application, in place of a stack of SaaS subscriptions and spreadsheets. The workflow shapes the software, and agents do the work that used to be a seat in someone else's product. ## Results in customer production ### Energy and critical infrastructure 91% less time spent searching documents, from a fully private model over 25 million internal records. ### Financial services Answer accuracy up more than 13% once every agent worked from one shared model of products and regulation. ### Automotive manufacturing Round-the-clock order intake from a voice agent that recognizes more than 170,000 part types and writes orders into the ERP. ### Public sector More than 96% answer accuracy on member questions, from an on-premises model that passed a national security certification. ### Insurance Document pre-processing fully automated end to end, with 85% user acceptance on underwriting answers. ### Consumer commerce 50,000 new customers from a generative AI advisor running across ten digital channels. ## Top-tier on every major cloud. Top-tier partner on AWS, Google Cloud, and Microsoft Azure, with more than 1,300 cloud certifications and SOC 2 Type II attestation. - Partner tier: AWS Premier Tier Services, Google Cloud Premier Services and Co-sell, Gold Microsoft Partner and Azure Expert MSP. - AWS competencies: AI Services, ML Services, Data and Analytics, and Migration and Modernization. - Google Cloud competencies: Artificial Intelligence, Data and Analytics, Infrastructure, and Application Modernization. Google Cloud Managed Service Provider and Authorized Training Partner. - Awards: AWS MSP Partner of the Year 2023, and Google Cloud North America Partner of the Year 2023 for Expansion. - Expert teams on Databricks, Anthropic, Snowflake, and OpenAI. ## Patterns we build - [Replacing a SaaS stack with an agentic deal desk](https://bespinus.github.io/bgus-ai-landingpage/use-cases/agentic-deal-desk.md): Generative and agentic AI. Quoting a deal touches five systems and three spreadsheets, and the current state of that deal lives in whichever one somebody updated last. Every tool we buy to fix the problem becomes a sixth place to look. - [AI sprawl across a dozen assistants, and no answer for the CISO](https://bespinus.github.io/bgus-ai-landingpage/use-cases/ai-sprawl-ciso.md): AI governance. There are at least a dozen AI assistants in use across the business. Our data loss tooling is monitor-only. If the board asks whether anything sensitive has gone into a public model, I do not have an answer. - [Years of expertise, searchable with the source attached](https://bespinus.github.io/bgus-ai-landingpage/use-cases/cited-knowledge-base.md): Generative and agentic AI. A decade of what we know is spread across documents, recordings, spreadsheets, and a few people's heads. New staff do not know what exists, and the two systems we have give two different answers to the same question. - [Documents into the system of record, without anyone keying the values](https://bespinus.github.io/bgus-ai-landingpage/use-cases/document-automation-system-of-record.md): Generative and agentic AI. Every month a senior person pulls statements from a dozen portals, reads each one, and keys the values into our system of record by hand. It is slow, it is error prone, and it is the most expensive data entry in the company. - [From enriched data to a qualified pipeline, with a person on every handoff](https://bespinus.github.io/bgus-ai-landingpage/use-cases/enriched-data-to-pipeline.md): Generative and agentic AI. We have spent real money enriching our data, and our sellers still start every conversation from scratch. Our personas are hypotheses nobody has tested, and what works for ten contacts falls apart at ten thousand. - [Forklift and pedestrian proximity safety on an active floor](https://bespinus.github.io/bgus-ai-landingpage/use-cases/forklift-pedestrian-safety.md): Orbit Vision AI. We have had near misses. Our controls are training, floor tape, and hoping the operator saw the person. We will not find out the tape stopped working until somebody gets hurt. - [Keeping an enterprise AI platform healthy after launch](https://bespinus.github.io/bgus-ai-landingpage/use-cases/managed-enterprise-ai-platform.md): Managed AI. We launched an AI platform and adoption outran the team that built it. Every new connector is one more access path nobody has assessed. - [A multi-agent assistant over nationwide public records](https://bespinus.github.io/bgus-ai-landingpage/use-cases/multi-agent-public-records.md): Generative and agentic AI. The answers we need sit in public records across every jurisdiction, in a different format in each one, and today the way we get them is a person reading. Nobody can staff that at the scale the question is being asked. - [Continuous defect detection across a multi-site plant network](https://bespinus.github.io/bgus-ai-landingpage/use-cases/multi-site-defect-inspection.md): Orbit Vision AI. We have already bought vision more than once. Each system graded one defect on one line, each came from a different vendor, and none of them talk to each other. The money is spent and we are still paying people to look. - [Giving teams the standards their coding agents will actually follow](https://bespinus.github.io/bgus-ai-landingpage/use-cases/standards-coding-agents-follow.md): Generative and agentic AI. Our developers are shipping AI-generated code faster than we can review it. Telling them to slow down is not working, and our standards live in a wiki that no agent has ever read. - [When unmanaged AI spend becomes an audit finding](https://bespinus.github.io/bgus-ai-landingpage/use-cases/unmanaged-ai-spend-audit-finding.md): AI governance. Internal audit asked which AI tools we use, who is paying for them, and what data goes into them. We could not answer any of the three. It is going in the report. ## Talk to an AI engineer. [Talk to an AI engineer](https://bespinglobal.us/contact) at Bespin Global. _Machine-readable summary for agents: provider = Bespin Global; page = Bespin results and use-case patterns; results = reported by sector; patterns = representative engagements; contact = ._