Business context
Understand where judgment, customer pressure, data, and repeated work already meet.
01 / Workflow Mapping
Find the workflows where AI can survive real constraints.
We start by learning how the work actually happens: the decisions, data, systems, handoffs, exceptions, and constraints. Then we identify the AI workflows and POCs with a real chance of becoming useful.
Workflow candidates
Find the POCs and processes where AI can create leverage without pretending the constraints are simple.
Operating constraints
Map the permissions, approvals, quality bars, failure modes, and systems the work has to respect.
Improvement path
Define how the workflow, model behavior, prompts, evaluations, and support process improve over time.
Common starting points
Strong candidates usually show up in document-heavy work, repeated decisions, and internal tools where people are already copying AI output back into the business by hand.
Knowledge operations
Turn scattered research, expert context, and policy detail into reviewable memos or answer systems.
Workflow automation
Move manual intake, routing, QA, and approval work into traceable review packets.
Internal AI products
Build focused tools for teams who need a repeatable process, not another one-off prompt.
02 / Prototype Build
Put a working prototype in the team's hands.
A useful prototype makes the potential of AI concrete. It gives the people closest to the work something real to try, criticize, and improve before the architecture gets heavier than the evidence.
Choose the proof
Pick the smallest slice that can show whether AI changes the work in a meaningful way.
Build with real context
Use representative documents, data, decisions, and constraints instead of a polished demo path.
Get user feedback
Put the prototype in front of the people who know the workflow and let them expose what breaks.
Tighten the loop
Use the feedback to improve prompts, retrieval, interface behavior, evaluation, and workflow design.
03 / Production Path
workflowapproved use case, users, exception paths, failure modes
datasource systems, documents, permissions, refresh pattern
modelretrieval, extraction, reasoning, critique, evaluation
operatemonitoring, maintenance, support process, release notes
measurequality, cycle time, adoption, rework avoided
Turn the prototype into a system the business can run.
If the prototype proves value, we help move it toward production: the integrations, permissions, evaluation loops, interface, governance, and maintenance service that keep the system useful after launch.
Production architecture
System design, data paths, permissions, vendor tradeoffs, and rollout plan.
Implementation hardening
Interfaces, connectors, evaluations, approval gates, monitoring, and documentation.
Continuous improvement
Model updates, prompt and retrieval tuning, quality review, and workflow iteration.
04 / Evidence
Measure what works, then adjust the system.
We do not treat launch as the finish line. A useful AI workflow needs a feedback loop: define what good looks like, measure how the system performs, learn where it fails, and adjust the workflow, model behavior, and interface.
MeasureQuality, cycle time, adoption, and avoided rework tracked against workflow goals.
ReviewUser feedback, exceptions, and failure patterns show where the system needs work.
AdjustPrompts, retrieval, interface behavior, and workflow routing improve from evidence.
ImproveMaintenance and model updates are treated as part of the operating service.
05 / About Us
A small Atlanta studio for AI systems that have to work inside real companies.
We help teams turn AI intent into usable systems: the workflow, the data path, the approval loop, the interface, and the evidence that the system is worth keeping. The work starts with business context and ends with technology the team can understand, operate, and improve.
- studio
- advisory, prototyping, implementation support
- focus
- workflows, data access, permissions, software delivery
- standard
- useful in production, explainable to operators
- base
- Atlanta, GA
06 / Connect
Bring a workflow, a POC, or the place AI already feels useful.
The first conversation is about whether there is a real implementation path: where the data lives, which tools matter, what quality means, how the model improves, and what should not be automated.
Send a note
hello@diagonal.dev
In the email, include the workflow or prototype, the current constraint, and what would make the first conversation useful.