Assess the opportunity
Understand where AI can create value and what has to be true for the investment to work.
Illustration of shared company knowledge connecting leadership, customer-facing teams, product, operations, finance, people, data, delivery, and feedback.
Build AI systems for deep work
AI advisory and implementation
We help organizations understand where AI can create value, implement systems that work with their real data and tools, measure results, and build the foundation to expand what works.
Start
Where to Begin
Start with the decision in front of you.
Some teams are deciding where to invest. Others are putting a system into production, measuring what changed, or expanding what already works. We begin with the current constraint and define the next useful step.
Put it into production
Turn the strongest opportunity into a working system connected to real data, tools, and users.
Measure what changes
Define success, track business and system performance, and learn what should improve next.
Expand what works
Give more teams the shared foundations, tools, and guardrails to build useful AI faster.
01
AI Advisory
Understand where AI can create value—and what it will take to make it work.
We advise teams on the systems, knowledge, and data foundations behind useful AI. That means identifying where AI can improve quality, increase output, or support better decisions; tracing the relevant data and knowledge sources; and making validation, versioning, permissions, ownership, and handoffs explicit before anyone commits to a build.
What we examine
The goal is a grounded view of the opportunity, the operating realities around it, and the foundation required to move forward responsibly.
Knowledge and data
Understand what information exists, where it lives, how current it is, and what the system can trust.
Value and opportunity
Identify where AI can improve decisions, generate useful output, extract new insights, or create capacity.
Readiness and risk
Surface gaps in access, quality, validation, permissions, governance, and operating ownership.
02
Implementation
Put AI into production against real data and real constraints.
We turn the strongest opportunities into working systems and integrate them with the tools and data the business already uses. That may mean querying internal knowledge, extracting insights, generating content, or supporting decisions—with the evaluation, security, review, and operating controls required for real use.
Validate the premise
Test advisory findings against real data, users, and business requirements.
Connect the context
Integrate the necessary data sources, knowledge systems, applications, identities, and permissions.
Build the capability
Create useful ways to query information, extract insights, generate content, or support decisions.
Operate it reliably
Add evaluation, human review, monitoring, documentation, security, and clear ownership.
03
Measurement Loop
Measure business value, not just model performance.
Launch begins the operating loop. We define what good looks like, track quality, speed, output, adoption, and failure modes, then use that evidence to improve the data, models, interfaces, and operating practices around the system.
DefineSet business outcomes, quality bars, approval rules, and failure thresholds.
MeasureTrack quality, cycle time, output volume, adoption, rework, and user feedback.
DiagnoseSeparate model problems from data, product, policy, or operating problems.
ImproveRefine the data, retrieval, prompts, interfaces, controls, and support model.
04
Expansion
Make the next AI capability faster to build than the first.
Every capability leaves behind data paths, permissions, building blocks, evaluation, and delivery patterns. The next one starts from that surface, so the shared foundation grows while the work required to ship falls.
Compounding sequence
shared layers 5 → 7 · reuse links 0 → 16
First working system
- new layers
- 5
- draws on
- 0
- shared surface
- 5 / 7
Next workflow
- new layers
- 1
- draws on
- 4
- shared surface
- 6 / 7
Another team
- new layers
- 1
- draws on
- 5
- shared surface
- 7 / 7
New use case
- new layers
- 0
- draws on
- 7
- shared surface
- 7 / 7
- F1Approved data + knowledge
- F2Identity + access
- F3Reusable building blocks
- F4Evaluation + learning
- F5Delivery patterns
- F6Retrieval + context service
- F7Human review + escalation
05
About Diagonal
Atlanta-based consultants with experience deploying AI at scale.
We are a group of Atlanta-based consultants with hands-on experience deploying AI within products serving millions of users. We work directly with leaders, operators, and technical teams, combining strategic advice with the implementation experience needed to understand what will actually work in production.
- studio
- AI advisory, implementation, measurement, and expansion
- experience
- hands-on AI deployment within products serving millions of users
- approach
- direct, practical, and close to the people operating the system
- base
- Atlanta, GA
06
Connect
Bring the opportunity, system, or constraint blocking the next step.
Tell us what you are trying to improve, what already exists, and where you are stuck. We'll reply with whether advisory, implementation, measurement, or expansion is the right place to start and what context would help us assess it.
Discuss an AI initiative
hello@diagonal.dev
Include what you are trying to improve, what systems or data already exist, the current constraint, and the decision you need to make. Keep the first note high-level; do not send confidential data, documents, or credentials.