AI Research & Consulting
AI research and consulting for SaaS businesses.
We help SaaS teams turn AI vision into real-world impact — every opportunity assessed on your own data, then a strategy, a budget and a roadmap that say what to build, what to skip, and what it returns.
Why AI initiatives stall inside SaaS businesses
When teams don’t share the same AI vision, efforts become fragmented, priorities conflict, and valuable resources get spent without moving the business toward its goal.
Research delays implementation
The build hits a wall — the approach doesn’t hold, and the team goes back to look for another one. Implementation waits on a search that would have been cheap before the build and is expensive inside it — and it waits again at the next wall.
Timeline overflows
A decision made twice is a build done twice. Each stop puts the date back, and the time it buys goes on rebuilding what already worked rather than on what is left to ship.
Overspending
The study is now billed at build rates, and the rebuilds sit on top of it. You have paid for both, and still cannot say what the finished thing returns.
AI consulting services for every role in your SaaS team
Every person in the room gets a plan tailored to their role, helping the entire team make informed decisions and move the AI vision forward together.
AI Strategy & Roadmap
AI Strategy & Roadmap
Check Out- Can we build this?
- How much do we need to spend on this?
- When does it pay back, and how long until we get there?
- What is the risk factor?
AI Stack
AI Stack
Check Out- What is the architecture?
- What tools do we need to use?
- Does it match our product and goals?
- What can our team build and maintain?
AI Governance
AI Governance
Check Out- Does the project match company policies and regulations?
- Do those policies match our geography?
- Who is the responsible person for the AI system?
- How much control do we have over the AI system?
AI Evaluation
AI Evaluation
Check Out- How much can we trust it, and how do we know?
- Is the model hallucinating, and how do we know?
- Is it grounded in the right sources, and does it stay that way?
- How should we build the system prompts, and how do we test them?
AI Security
AI Security
Check Out- Is our system secure against attacks?
- Does it have enough defence in depth?
- How do we reduce the attack radius?
- What is the risk of a successful attack?
Our approach: AI research, evaluation, and adoption planning
We analyze insights, measure the impact, and plan the right solutions for your business.
Evaluation
We list every job AI could do in your business. Then we test each one for cost, accuracy, and how often it runs. Most fail. The rest are worth building.
AI Opportunities
We measure what AI adds over the tools you have today, on your own data. You get a ranked table, with the numbers behind every row.
Roadmap
You get a build plan for the top job: how it works, how accurate it must be, and how to tell if it stops paying. Your team builds it and keeps the code.
Applied Research
The frontier moves every few weeks. We keep testing new models and methods against your own evals, so a job that failed on cost last quarter reaches you the week it clears.
AI case studies from real SaaS and software products
Real outcomes from real deployments — each measured against what the product already did without AI.
What clients say about us
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Frequently asked questions about AI consulting
What do I actually get at the end?
Measured findings — not a slide deck. Every AI opportunity in your product, scored on your own data, ranked by payback, with the arithmetic shown for each row and a build plan for the one at the top. Your team keeps all of it.
How long does an engagement take?
Most first engagements run two to four weeks from kickoff to the findings landing on your desk. The range is mostly about how fast we can get access to your data and the people who know it.
Do you build the thing, or just tell us what to build?
The default is that we measure and specify, and your engineers build it — the roadmap is written for them, with the accuracy bar and the failure signals spelled out. If you'd rather we built the first one, that's a separate conversation.
What if the answer is that we shouldn't build anything?
Then that's what the findings say. Most candidate use cases don't clear the bar once you price them against the tools you already run, and a firm that will tell you to skip one is the only kind worth paying for the ones it tells you to build.
What do you need from us to start?
Read access to the data behind the workflows you care about, and a couple of hours with the people who run them. No production access, no code changes, and nothing that has to ship before we can measure.
How do you handle our data and IP?
Under an NDA, on the narrowest access that lets us measure, and your data never trains anything. Everything we produce is yours. If you have a security review, we'll go through it before any access is granted.
What does it cost?
It's scoped per engagement — the driver is how many workflows are in play, not headcount or seats. Bring the budget you had in mind to the intro call and we'll tell you straight away whether it buys anything worth having.
What happens after the findings land?
You own them and you can act on them without us. Models get cheaper and better every few months, so the option we keep open is continuous discovery: we re-run your list against what's new and flag anything that just crossed the line.
Let's start planning your AI vision.
We will help you in any stage of your AI journey, if your team is just starting to explore AI or if you are already building and need guidance.