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Permio: AI-powered permit workflows for Service First Permits
Service First Permits (SFP) is one of the leading permit management providers in the United States. The company partners with construction teams, architects, and developers to oversee every stage of the permitting process — from initial strategy to final approval. SFP coordinates with design teams, prepares documentation in compliance with regional requirements, submits to jurisdictions, and manages the full review cycle. Their goal is simple: make the permitting journey faster, clearer, and less resource-intensive for their clients.
Geniusee partnered with Service First Permits to build Permio — an AI-powered platform that automates permit workflows, digitizes documentation, and accelerates approvals across 240+ jurisdictions and 380+ agencies.
Business context
SFP approached Geniusee with a goal to digitize and automate core parts of their permitting process. Their clients needed a modern, intuitive platform to access up-to-date information on region-specific requirements, permit statuses, documentation workflows, and timelines.
In addition, the company wanted to introduce an AI assistant to help process tasks in parallel, accelerating permit acquisition and reducing the reliance on manual work.
Key challenges:
Heavy manual workflows. The permitting process involved numerous repetitive tasks and unstructured data handling, which limited scalability.
Delays due to lack of parallelization. Core tasks couldn’t run concurrently delaying permit review, document prep, and client coordination.
No comparable digital solution on the market. SFP needed to build a custom system from scratch, tailored to their internal logic and compliance needs.
Solutions we implemented
Geniusee collaborated closely with Service First Permits through daily meetings, both internally and with the client team. These included focused sessions with business analysts, developers, and QA leads to clarify requirements, resolve blockers, and adapt to shifting priorities.
This iterative communication helped us stay aligned and responsive throughout development. Below is a breakdown of the key solutions delivered:
Project tech stack
Languages
Backend
State management & APIs
Routing
Maps
Documents & PDF management
Analytics & monitoring
Utility libraries
Geniusee team
Development team
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QA specialists
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Backend engineers
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Frontend engineers
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Data engineers
Product team
Features
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AI-powered chat assistant
Context-aware messaging, threaded discussions, file sharing, and smart replies tailored to project data.
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Advanced PDF management
Preview, annotate, edit, and collaborate on documents in-browser with version control and multi-format support.
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Visual scheduling tools
Gantt charts, calendar integration, and task dependency views to streamline team coordination
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Real-time updates and alerts
Notifications for document changes, project status, team mentions, and audit events
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Analytics and reporting dashboards
Track usage, identify bottlenecks, and generate reports for operational oversight
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Multi-user onboarding
Easy registration, Google sign-in, email invites, and permission-based access control
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Map and location support
Integrated Google Maps to manage jurisdiction-specific projects and data
Results
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AI-assisted processing reduced delays. Tasks that previously required manual follow-up were handled in parallel via the AI assistant, improving processing time by up to 200%.
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Clients can now submit documents in the correct format, track status, and receive real-time updates, cutting turnaround time significantly.
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Document accuracy and compliance have been improved. Smart algorithms flagged missing data and compliance risks early in the process.
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User growth increased by 2000%. Enhanced UX, automation, and feature depth helped attract new users, particularly during exhibitions and post-UAT.
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Stronger feedback from beta and UAT rounds. The second round of testing showed significant improvement in user experience, chat usefulness, and navigation flow.
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The final product enabled the client to demonstrate business scalability and product-market fit to potential investors.
Challenges Geniusee encountered
Early-stage UAT failures due to incomplete features
Fragmented workflows and unclear feature dependencies
Performance issues with PDF.js and mapping libraries
Unstable scope and shifting priorities mid-sprint
Technical complexities of integrating LangChain and real-time messaging
Client-side requests to release before critical components were stabilized
How Geniusee resolved these issues
Refined key features and priorities, with AI becoming central to the product
Streamlined processes for faster development cycles and parallel workstreams
Maintained close communication with the client to resolve blockers quickly
Introduced a structured phase 2 plan to stabilize features post-launch
Selected and tested the most efficient third-party libraries to reduce load times
