Proof tied to business and operating reality
Case Studies
In Context
The point isn't just what got shipped. It's the whole arc: what we built, how it ran after launch, and what happened when ownership transferred. Some engagements predate the current monthly workflow-automation model. We say so honestly rather than reframe the past.
Three of our recent engagements have produced public case studies. Each shows the full arc: what we built (Build), how it ran under SLA (Operate), and what happened at handoff (Transfer). Combined scope: 345K+ lines of production code, 122 deployed services, and two enterprise multi-tenant platforms.
Fidelius
Enterprise Memory And Retrieval Platform
Pattern Mix
Build shipped the secure retrieval platform and the operating controls to production. Operate kept it improving cycle over cycle: multi-tenant isolation, observability, and downstream product readiness, so the platform did not decay after launch.
Business Context
The job wasn't to build a RAG application. It was to build a secure memory and retrieval foundation that could support multiple enterprise workloads, hard isolation boundaries, and future product lines on a single platform.
Business Relevance
The client needed a reusable platform capability that would shorten future product delivery, not another one-off AI feature funded out of an innovation budget.
Solution
- Designed, developed, and deployed the Fidelius memory service
- Built a cognitive-science-based memory system with four-way retrieval
- Created a multi-tenant security framework with hard tenant isolation
- Implemented defense-in-depth safeguards around code execution
Approach
- 1.Sequenced the engagement around platform risks first: data isolation, retrieval quality, and execution safety.
- 2.Made architecture decisions that supported future product expansion, not one-off feature delivery.
- 3.Built observability and operating boundaries alongside the platform so scaling didn't depend on manual intervention.
Impact
A shared enterprise platform capability that supports multiple downstream AI experiences, instead of rebuilding memory and retrieval infrastructure for every new use case.
Business And Operating Outcomes
- +A reusable foundation for multiple AI products, not a single implementation
- +Lower platform risk because isolation and observability landed before downstream scale
- +A workable operating model for secure multi-tenant AI delivery
“The platform gave us a single secure retrieval layer to build multiple AI products on, instead of starting from scratch each time.”
— Engineering Lead, Enterprise AI Platform
Reparo
AI Diagnostics In A Field-Service Workflow
Pattern Mix
Build covered the full engagement: workflow framing, business case, and shipping the production experience inside SMS and MMS so technicians could use it without changing workflows. The client retained operational ownership at launch. This engagement ran before Uplift offered the Operate retainer.
Business Context
Turn expert troubleshooting knowledge into a workflow field technicians could use in the moment, through channels they already had open, without forcing app adoption.
Business Relevance
Every extra minute diagnosing a job delayed service completion and made expert knowledge harder to access at the point of work.
Solution
- Built an AI diagnostic agent with a natural-language SMS and MMS interface
- Integrated vision models for image analysis during troubleshooting
- Developed a multi-tenant SaaS architecture with Stripe billing
- Implemented idempotency patterns for reliable message handling
Approach
- 1.Kept the experience inside SMS and MMS so the workflow matched how technicians already worked.
- 2.Designed the architecture for reliability first because message duplication and bad field conditions were operating realities, not edge cases.
- 3.Combined vision and language to reduce diagnosis friction, not to showcase model complexity.
Impact
Diagnostic support moved closer to the job site. Less delay, expert guidance accessible without a new interface or workflow burden.
Business And Operating Outcomes
- +Lower adoption friction because the tool lived inside what technicians already used
- +Troubleshooting support available at the point of service, not back at the office
- +A delivery pattern that scales to broader field-service workflow automation
“Our technicians started using it the same day it launched because it worked through the messaging app they already had open.”
— Operations Director, Field Services Company
Ask Clay
Fast, Trusted Q&A For A Growing Content Corpus
Pattern Mix
Build focused on the retrieval workflow, validating answer quality, and shipping a user experience that stayed fast and trustworthy as the corpus expanded. The client retained operational ownership at launch. This engagement ran before Uplift offered the Operate retainer.
Business Context
Balance accuracy, retrieval speed, and a cross-platform experience well enough that the product could answer rule questions with confidence during live gameplay.
Business Relevance
The value wasn't just speed. It was keeping product trust intact by delivering reliable answers fast enough to be used in the middle of a live session.
Solution
- Developed a full-stack RAG system with sub-5ms vector search
- Built a cross-platform mobile application for iOS and Android
- Integrated LangSmith tracing for observability and debugging
- Indexed comprehensive rule sets for more than 42 board games
Approach
- 1.Focused first on retrieval latency and traceability because trust depended on both speed and answer quality.
- 2.Designed around a growing content corpus so new games could be added without redesigning the stack.
- 3.Wired tracing and debugging in early to keep iteration grounded in real query behavior.
Impact
A hard retrieval problem turned into a repeatable product workflow that could expand content coverage without degrading user trust or usability.
Business And Operating Outcomes
- +Higher product trust because speed and answer quality moved together
- +An operating model for adding new content without redesigning the system
- +Clearer observability into how real users queried the experience
“Speed and accuracy were tight enough that players trusted it inside the first game. It felt like having a rules expert at the table.”
— Founder, Board Game Platform
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“They sat with our team, found the work nobody was talking about, and shipped a tool inside our existing systems within two weeks.”Operations Director
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