AlignDrift
How one consultant turned a calendar-bound methodology into a platform serving 5+ enterprise clients at once, without adding headcount.
Casey Powers
CEO, AlignDrift™
“We're selling a number that sits next to revenue in the boardroom. If that number drifts because the AI had a different mood, we don't have a product. Dojo Labs built it so the score is computed the same way every time, for every client.”
Casey Powers
CEO, AlignDrift™
Measurable Outcomes
that drive ROI.
5+
Enterprise Client Orgs
8
Alignment Dimensions
90%
Admin Overhead Reduction
500+
Assessments Completed
By integrating our computation layer, AlignDrift transformed from a services-heavy model to a scalable, automated platform.
Client Overview
About AlignDrift
Casey Powers spent years refining an organizational alignment methodology, a way to measure how well employees across departments understand and execute on company strategy, scored across eight dimensions. The methodology worked under his direct delivery. To scale it into AlignDrift™, the executive metric product he sells today, it had to leave his calendar.
Casey wasn't building a survey tool. He was building an executive metric, the Organizational Alignment Score (OAS™), positioned alongside revenue as something a CEO governs at the board level. AlignDrift's own tagline: the one metric your other metrics depend on.
That positioning sets the bar. An executive metric has to behave like one: reproducible across clients, defensible under scrutiny, identical for the same inputs every time. That's the system Dojo Labs built.
Industry
Organizational Development / Executive Metrics
Client
Casey Powers, CEO at AlignDrift™
Target Market
Mid to large enterprises, executive leadership, OD consultants
Engagement Type
Multi-tenant SaaS platform, full build
Status
In production. Multiple enterprise clients live.
The Problem
The Challenge
Casey came to Dojo Labs with the methodology already proven through years of consulting engagements. He knew exactly how the OAS should be calculated. The question was whether it could be encoded into software without losing what made it credible. Three problems had to be solved at once.
Scoring had to be deterministic. AlignDrift sells the OAS to enterprise leadership, and the metric appears in board reports. If the same employee responses produced different scores on different runs because an LLM happened to interpret the data differently, the entire product collapses. Generation-based scoring was off the table from day one.
The methodology had to be reproducible across clients. A score that means one thing for a 50-person company and something else for a 500-person company isn't a metric, it's a vibe. The eight-dimension framework had to apply identically across organizations of different sizes, structures, and verticals.
The whole thing had to scale beyond Casey. As long as Casey was personally involved in scoring, AlignDrift was a consultancy with a calendar bottleneck. To position OAS as enterprise SaaS, and to compete with $20K+ consulting engagements rather than against them, the platform had to run without him in the loop.
The Core Problem
The brief, in one line: get Casey out of the delivery loop without losing what made his methodology credible. As long as he personally scored every engagement, AlignDrift was a consultancy capped by his calendar. The Employee had to run the assessment, score it the same way every time, and produce the board-ready report, so one consultant could serve enterprise clients at the scale of a team.
What We Built
Our Solution
Dojo Labs designed and built the AI Employee that runs AlignDrift™ in production. Three integrated layers take an enterprise from survey distribution to a board-ready OAS in a single workflow, with no consultant in the loop.
01. Deterministic Scoring Engine
Every employee response runs through the same scoring path, every time. The LLM handles what LLMs are good at: interpreting open-ended language and mapping it to the eight-dimension framework. The actual scoring (comparison against Casey's baselines, drift calculation, departmental aggregation, the OAS itself) runs through a deterministic engine. Same responses produce the same OAS. Across clients, across runs, across time.
Input: Employee responses submitted via mobile-optimized portal
Processing: LLM interprets responses, then a deterministic engine computes scores against Casey's baselines
Output: Individual alignment scores, departmental drift metrics, OAS at the organizational level
02. Multi-Tenant Operations Platform
A single console for AlignDrift™ to run unlimited concurrent enterprise engagements. This is what unlocked the enterprise pricing tier. Casey isn't trading hours for dollars anymore, the platform runs each engagement at near-zero marginal effort.
Survey configuration tied to the proprietary baselines
Automated email distribution with reminder sequences
Real-time response tracking by department and management level
Role-based access separating AlignDrift admins, client-side managers, and respondents
03. Executive Reporting Layer
One click produces a branded executive PDF with OAS scores, drift indicators, departmental breakdowns, and recommendations mapped to identified alignment gaps. The output is what a CEO sees. The format is what a consulting firm charges $20K+ to produce.
Branded output with alignment scores and departmental breakdowns
Executive summary auto-generated from computed metrics (not generated numbers)
Recommendations tailored to identified alignment drift patterns
Replaces the manual report formatting that previously consumed days per engagement
Tech Stack
Technologies Used
| Layer | Technology | Role |
|---|---|---|
| Backend API | PHP / RESTful Architecture | Service layer, routing, business logic |
| Language Layer | OpenAI API + Anthropic Claude | Semantic interpretation of open-ended responses |
| Scoring Engine | Deterministic PHP Module | Baseline comparison, OAS computation, drift metrics |
| Database | MySQL (Multi-tenant) | Survey data, response history, longitudinal alignment trends |
| Infrastructure | AWS (EC2, RDS, CloudWatch) | Hosting, persistence, monitoring |
| Auth | JWT + Role-Based Access | AlignDrift admin, client manager, respondent tiers |
| Reporting | Custom Branded Template Engine | Executive PDF output |
| SMTP with Reminder Sequencing | Automated distribution |
Why This Architecture, Specifically
An LLM (whether OpenAI's, Anthropic's, or anyone else's) asked to score employee responses against a framework will produce a number. That number is the model's prediction of what a score should look like given the prompt. It is not the result of comparison. Run the same data through the same model an hour later and you may get a different number, with no way to explain why.
For a one-off insight, that's tolerable. For a metric Casey is selling as governance-grade, a number that goes in front of CEOs alongside revenue, it's fatal. The day one of Casey's clients runs the same data twice and gets two different OAS values is the day AlignDrift loses its credibility.
So the architecture splits the work. The LLM does language. The deterministic engine does math. The OAS is computed against Casey's baselines using fixed logic, the same way every time, for every client.
The Transformation
Before & After Dojo Labs
Before
Methodology lived in Casey's head and a stack of PDFs
One or two concurrent engagements, capped by Casey's calendar
Manual scoring meant Casey personally validated every output
Manual report formatting consumed days per engagement
No path to enterprise SaaS pricing
After
Methodology encoded into a multi-tenant platform
Multiple enterprise clients running concurrently
OAS computed deterministically: same inputs, same score, every time
Reports generated in one click
Competing in the $20K+ engagement tier as productized SaaS
Roadmap
What's Next
If you're productizing a methodology, framework, or scoring system into AI-powered software, and especially if the output is a metric you're going to put in front of executives or sell as governance-grade, the architecture matters more than the model.
LLMs generate plausible numbers. They don't compute reliable ones.
For products where the score is the product, that distinction is the difference between something a CEO trusts and something they quietly stop using.
Dojo Labs builds the computation layer that makes those products defensible. The same approach we apply on financial extraction, sales quoting, and trading systems.
Building a product where the number matters? Book a 30-minute call. We'll look at where your scoring logic sits today, where it's exposed to drift, and what the computation layer should look like.
Ready to build something like this?
Book a 30-minute call. We'll discuss where your AI handles numbers, identify hallucination risks, and map out your computation layer.
Book a Free Discovery Call