Generative AI That Produces
Measurable Business Outcomes.
Not demos. Not proof-of-concepts that never reach production. We apply AI to the use cases that change how you work, what you sell, or what it costs you to operate.
The Gap Between AI Excitement and AI Results
Most organizations experimenting with generative AI are experiencing some version of the same set of problems. The technology isn't the barrier. The architecture is.
Situation One"AI pilots succeed in demos but fail in production"
The model works beautifully on curated test data. Then it hits real production data, with its inconsistent formats, missing context and edge cases, and starts producing errors that erode trust fast.
Situation Two"Nobody can identify which use cases are actually worth building"
There's excitement about AI across the organization, but no clear framework for deciding which use cases justify the investment and which are chasing novelty. Resources get spread thin.
Situation Three"Hallucinations and accuracy concerns block adoption"
Legal, compliance, and leadership won't approve AI workflows because outputs can't be trusted. Without retrieval architecture and guardrails, that concern is valid, and adoption stalls.
Situation Four"AI can't connect to the systems that hold your data"
The LLM is capable. But it can't see your documents, your customer records, or your operational data. Without integration architecture, it's just answering generic questions.
Situation Five"Governance and risk frameworks don't cover AI yet"
Your organization's risk, compliance, and audit frameworks weren't written for AI. Without an AI governance layer, leadership can't approve production deployments.
Situation Six"Adoption is low because the tools don't fit workflows"
AI tools deployed without workflow integration get used once and abandoned. The technology has to meet people where they work, not require them to change how they work to use it.
Everything You Put Into Off-the-Shelf AI Tools Is Leaving Your Organization
Most enterprise teams don't realize this: every prompt sent to a public AI model, every legal contract pasted in, every engineering diagram uploaded, every strategic plan run through a public chatbot, is being used to train that model. Your IP, your customer data, your competitive strategy. It's not staying inside your walls. The question isn't whether to use AI. It's how to build it in a way that's safe, governed, and scales with you.
What We Actually Build
Generative AI engagements at CleanSlate start with use case validation: finding the specific problems where AI produces the most measurable value. Then we build the architecture to support them.
Use case identification and ROI modeling
We evaluate your operations for AI use cases against three criteria: data availability, process fit, and measurable value. The ones that pass all three get built. The rest don't.
RAG architecture and LLM integration
Retrieval-augmented generation connects the model to your actual data: documents, knowledge bases, records, so it answers questions accurately instead of hallucinating plausible-sounding responses.
AI governance and guardrails
Output validation, human review workflows, audit logging, and risk controls that give compliance and leadership the confidence to approve production deployments.
Workflow integration that drives adoption
AI deployed inside the tools your team already uses, not a separate product they have to learn. Adoption that compounds rather than fading after the launch announcement.
to memory company
"A company that automated what it previously had to hire for was able to pivot from a yearbook company to a memory company, landing contracts with major cruise lines and National Geographic. The AI didn't replace what they did. It multiplied what was possible."
How Generative AI Engagements Go Wrong
The vendors making the biggest promises in AI are often the ones with the least production experience. Here's what to look for.
"We can implement this in a week"
Production-grade AI requires governance, security architecture, integration work, and testing against real data. Any vendor promising a week-long implementation hasn't built something that will work past the demo.
Pre-canned agents for every use case
Generic agents applied to specific workflows produce generic results. The value of AI comes from how well it's integrated with your specific data, processes, and workflows, not from an off-the-shelf agent.
The six-month AI strategy document
A lengthy strategy engagement that produces a roadmap document, not working software, is already obsolete when delivered. Strategy and delivery should happen together, in short cycles that produce real value at each step.
Six-to-eight week cycles toward real value
We deliver working AI in short cycles, not multi-year roadmaps. Each cycle produces something your team can use, measure, and build on. You'll know whether it's working before the engagement is over.
How to Evaluate Your Generative AI Options
Before you choose an approach, or a partner, understand what the real tradeoffs are. The table below is an honest comparison, including the cases where doing it yourself may be the right answer.
| Approach | Do It Yourself | Other Partners |  |
|---|---|---|---|
| Starts with use case validation | ✕ Most internal AI projects start with the model, not the use case. The result: technically impressive demos that don't survive contact with real workflows. | ~ Often, but "use case workshops" can become 6-month strategy engagements with no working software. | ✓ Always. We evaluate use cases against three criteria: data availability, process fit, and measurable value. The ones that pass all three get built. |
| Governance and security built in | ✕ Typically added late, if at all. Most internal builds focus on capability before compliance. | ~ Addressed, but often as a separate governance engagement after the build. | ✓ Yes. Output validation, audit logging, access controls, and risk frameworks are built into the architecture from day one. |
| Delivery cycle | Typically longer than expected when starting without use case validation and data readiness. | Extended by strategy-heavy phases. Working software arrives late. | ✓ 6–8 week cycles toward working software. You have something in production before the engagement ends. |
| Model agnostic | ✕ Usually defaults to whatever model the team is most familiar with. | ~ Often tied to platform partnerships. AWS, Azure, or Google, which shapes recommendations. | ✓ Yes. We know the tradeoffs between Anthropic, OpenAI, AWS Bedrock, and Azure OpenAI. We recommend based on your use case. |
| Your team can run it after | Yes, but requires sustained internal investment to maintain and extend. | ~ Varies. Large firm builds can create dependency on ongoing support contracts. | ✓ Yes. We build for operability. Your team can maintain, monitor, and extend what we build without ongoing dependency. |
| Typical engagement cost | Lower direct cost, higher opportunity cost and risk of extended timelines. | ✕ High cost for strategy-first engagements, with value arriving late and documentation that can be obsolete by delivery. | ✓ Scoped to the use case. AWS funding often offsets significant cost. No multi-year commitment required. |
| Best fit when... | You have dedicated ML engineering capability and can sustain a long internal development horizon. | You need a large team managing enterprise-scale AI infrastructure across multiple business units. | ✓ You want working AI in production within weeks, governed, secure, and built on your actual data. |
We Start With Use Cases, Not Models
Most AI engagements start with a model selection or a technology choice. We start with a specific business problem that has measurable value, available data, and a realistic path to production.
Use case validation first
We evaluate use cases against data availability, process fit, and measurable value. The ones that pass all three get built. The rest don't consume your budget.
Governance built in from day one
Output validation, audit logging, and risk controls are part of the architecture from the start, not a compliance layer added after production fails.
Six-to-eight week delivery cycles
You see working software in production before the engagement ends, not after a 12-month strategy document. Each cycle builds on the last.
Generative AI requires a data foundation first.
If your data is siloed, ungoverned, or inaccessible, no model choice will fix that. COBRA™ tells you where your foundation stands.
Generative AI on AWS, Explained by the People Who Build It
Four short videos with Nathan Liston, who leads our AI practice. Watch one or all four, in any order.
From Our Resources
Not Sure AI Is Where to Start?
These articles are for buyers earlier in the process, still naming the problem or evaluating approaches before committing to a conversation.
Are Your Gen AI Experiments Stuck in POC Limbo?
Many organizations are experimenting with Generative AI but struggling to move pilots into production. Learn the signs your Gen AI initiatives are stuck and what to do about it.
Read articleCan't Connect AI to Real Business Value? Here's Why
AI tools deployed, models built, dashboards live, and none of it is changing how the business operates. That's an adoption problem, not a technology problem.
Read articleShould You Upskill Internally or Partner for AI Success?
An honest look at the internal upskilling vs. external partnership decision for enterprise AI capability, with real trade-offs for each path.
Read articleAI Doesn't Have to Feel Like an Endless Commitment
The fear we hear most often: "This is going to require perpetual investment with no clear finish line." That's a legitimate concern about how most AI engagements are sold. Our engagements work in six-to-eight week cycles. Each one producing working software your team can use and measure. You'll see results before the engagement ends, not after a 12-month strategy document. A COBRA™ assessment identifies the use cases worth building, validates your data readiness, and gives you a specific roadmap with defined outcomes. In most cases, it's fully funded.