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AI Workflow Automation
AI automation and workflow automation, delivered for multiple clients, with human review and oversight built into each process.
Business goals
Our clients include a truck equipment manufacturer, a commercial general contractor and a venture investment firm. Their staff spend real hours on repetitive office work: reading incoming documents, sorting requests, moving details between systems and writing routine summaries. These companies wanted AI to take on that load, but not as an unsupervised experiment. They needed workflows that fit their operations, clear approval points and a way to manage how AI was being used.
Project overview
Hexagon delivered AI automation, AI management and workflow automation for multiple clients. We looked at where staff were repeating work, then built automations that read inbound material, pull out the useful details, draft a response or summary and pass it along for approval. Workflows move tasks between people and systems according to set rules. Management tools record what the AI did and allow staff to review its output, correct it and adjust how it behaves.
Challenges faced and solved
AI saves time only when it is pointed at a real process and someone stays accountable for the outcome.
Skilled people doing clerical work
Estimators, project staff and analysts lose hours reading documents and retyping details into other systems. That work is necessary, but it pulls experienced people away from the judgment calls they are paid for.
We automate the reading and data capture steps, so staff begin with extracted details and a draft and then apply their own judgment.
Requests that sit in shared inboxes
A parts inquiry, subcontractor quote or company update lands in a general mailbox. Someone has to read it, decide what it is and forward it, and that delay repeats dozens of times.
We use AI to classify inbound messages and route them by rule, so each request reaches the responsible person with a short summary attached.
Fear of unchecked AI output
A wrong figure in a bid or a client message can do real damage. Business owners reasonably hesitate to let software act on their behalf if no one can see its work.
We add review steps before anything consequential goes out, with confidence flags and clear ownership, so people approve the output that matters.
AI use with no oversight
When employees adopt AI tools on their own, nobody knows what data is being shared, which tasks rely on it, or whether results are any good. That leaves leaders exposed.
We provide AI management with access controls, activity logs and output review, so leaders can see how AI is used across the workflows we built.
Features
| Feature | Description |
|---|---|
| Operations and office staff | |
| 1. Document Data Extraction | AI reads incoming documents such as quotes, forms and orders and pulls key fields, reducing retyping. |
| 2. Inbound Message Classification | Emails and requests are labeled by type and urgency, so routine items don't wait for someone to sort them. |
| 3. Automatic Routing | Classified work goes to the right person or queue by rule, cutting the time spent forwarding things. |
| 4. Draft Replies | Routine responses are drafted from approved information, leaving staff to check and send rather than write from scratch. |
| 5. Document Summaries | Long documents and message threads are condensed into short summaries that highlight what needs a decision. |
| 6. Knowledge Search | Staff ask questions in plain language against approved company documents, instead of hunting through shared folders. |
| Managers and approvers | |
| 7. Approval Queues | Items that need a person's sign-off wait in one list, with the source material next to the AI's output. |
| 8. Confidence Flags | Results the system is less sure of are marked for closer review, directing attention where errors are more likely. |
| 9. Workflow Status Tracking | Managers see where each item stands and which step is holding it up. |
| 10. Exception Handling | Items that don't match the rules go to a person rather than being forced through, so unusual cases get real attention. |
| 11. Scheduled Reporting | Recurring summaries are compiled and delivered automatically, removing a weekly chore from someone's calendar. |
| AI and systems administrators | |
| 12. Activity Logging | Each automated action is recorded with its input, output and any human edit, so results can be reviewed later. |
| 13. Prompt and Instruction Management | Instructions used by the AI are kept in one managed place and updated in a controlled way. |
| 14. Role-Based Access | Permissions decide who can run, review or change a workflow, and which data each automation can reach. |
| 15. Output Quality Review | Samples of AI work are checked and corrections fed back, so quality problems are spotted early. |
| 16. Usage Monitoring | Administrators can see which workflows are used and how often, helping them decide what to expand or retire. |
| 17. System Connectors | Workflows connect to email, documents and business systems, so information moves without manual copying. |
How the platform is put together
Each layer has one job.
The result
Our clients have moved repetitive reading, sorting and drafting into managed workflows, with people still approving what matters. AI activity is recorded and visible to those responsible for it.
| Area | Before | After |
|---|---|---|
| Inbound documents | Read and retyped by staff | Key details extracted for a person to check |
| Request handling | Sorted by hand from shared inboxes | Classified and routed by rule |
| Routine writing | Drafted from scratch each time | Draft prepared for review and sending |
| AI oversight | No shared record of AI use | Access controls, logs and output review |
How we took it from idea to launch
Good software follows a process. Here are the six stages, and what happens in each one.
Discovery & Requirements
Goal: Define the product vision, target audience and core business requirements.
Key activities- Stakeholder interviews
- Market and competitor research
- Defining user personas
- Drafting technical specifications or a Product Requirements Document (PRD)
Design (UX / UI)
Goal: Create an intuitive user experience and visual interface.
Key activities- UX design: information architecture, user flows and wireframes
- UI design: high-fidelity mockups and interactive prototypes in Figma or similar tools
- Design system: colors, typography and component libraries
Development: Frontend & Backend
Goal: Build the functional application using agile sprints.
Key activities- Backend: set up databases, design APIs (REST, GraphQL), implement server logic, and configure authentication and authorization (OAuth, RBAC)
- Frontend: build responsive interfaces and connect components to backend APIs
- Version control: manage code through Git and GitHub
Security & Optimization
Goal: Harden the application against vulnerabilities and boost performance.
Key activities- Security: address the OWASP Top 10, including injection, XSS and CSRF, enforce encryption in transit and at rest, and implement rate limiting
- Optimization: code splitting and lazy loading, database indexing, API caching and load testing
- QA and testing: unit, integration and end-to-end tests
Beta Release
Goal: Deploy to a controlled group of real users to catch edge-case bugs and gather feedback.
Key activities- Set up staging and production CI/CD pipelines
- Launch a closed or open beta with real users
- Monitor errors through observability tools
Public Release (Launch)
Goal: Roll the software out to the general public.
Key activities- Final production deployment
- DNS configuration
- Marketing and onboarding rollout
- Post-launch performance and security monitoring
Business benefits
Experienced staff spend less of their day on clerical steps.
Requests reach the right person sooner.
People approve the outputs that carry consequences.
Logs show what the AI did and who signed off.
Conclusion
Useful AI in a business rarely looks dramatic. It looks like a quote read before anyone opens it, a request already in the right queue, and a draft waiting for an expert's approval. Hexagon built these workflows for clients in manufacturing, contracting and investment, keeping people in charge of the decisions that carry risk and giving leaders a record of what the AI did. If you have a task your best people repeat every day, describe it to us and we can discuss whether AI fits.
Talk to us about your projectRelated services: business process automation · AI and machine learning · custom software development
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