Your product already works. Now AI has to work inside it. Established vertical SaaS products carry years of workflows, business logic, integrations, permissions and data. Datics specializes in brownfield AI: production AI inside the product you already operate, without a rebuild.
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The problems we solve
Three problems that stop an established product from becoming AI-native, and what removes each one.
Blocked: You know the product needs to become AI-native. The roadmap keeps slipping.
Unlocked by: A sprint that turns the mandate into a prioritized, sequenced plan.
Blocked: Your AI produces intelligence. Your customers still have to do the work.
Unlocked by: Capability wired to your APIs, permissions and approvals, so it completes work.
Blocked: The AI works in the demo. Real production constraints stop it from shipping.
Unlocked by: Tenancy, permissions, audit and deployment designed in from week one.
One reference pattern
One reference pattern for supervised AI inside an established product. Our own build; in your product it ships under your name, on your APIs and inside your permission model.
One reference pattern, not a product
The product as it ships today: every decision reached by filtering, reading and clicking.
The model is only one part of production AI. The harness is what makes it safe and useful inside mature software.
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What it unlocks
More value from the product you already own
AI makes existing functionality easier to reach and makes new workflows possible inside the same product.
More reasons for customers to stay
Intelligence becomes part of the daily workflow rather than another tool customers have to adopt.
A stronger competitive moat
Your domain logic, data relationships and integrations are assets generic AI-native entrants cannot reproduce quickly.
Proof
Mature vertical software, existing customer workflows, multiple integrations and regulated deployment boundaries.
Vertical SaaS, dental and veterinary practice communications
Established communication intelligence software serving dental and veterinary practices. AI needed to become part of existing practice workflows rather than a separate application.
Vertical SaaS, dealership operations
A multi-year product engineering partnership inside a live dealership platform: existing customer workflows, dealership operations, integrations, appointments, reporting and customer intelligence.
The AI did not arrive as a separate tool. It showed up inside the product our customers already log into every morning.
Built for production, not prototypes.
Every figure here counts work already delivered. We do not publish estimated outcome percentages.
Why Datics
AI and data were foundational at Datics from 2018, and we can work on the application underneath the AI as well as the AI itself.
We do not start from an empty repository. Our work happens inside products that already carry customers, contracts, integrations and a permission model that cannot be casually changed.
8 AI builds inside existing products, 5 of them vertical B2B SaaS companies.
Datics began in data science and AI in 2018 and grew into SaaS product engineering. Generative AI landed exactly where those two capabilities converged, rather than being added to a generic consultancy.
Founded October 2018 by two founders with AI and data science backgrounds.
Model layer, harness, application architecture and production infrastructure handled by one team. Most AI capability stalls on the product beneath it, which is the part we can actually change.
Around 21 of 35 engineers work with LangGraph day to day. AG-UI contributor; the AWS Strands integration was first built by a Datics engineer, and we work with CopilotKit.
That complexity is also your advantage. Years of domain logic, integrations, workflows, permissions and customer context give established SaaS products something new AI-native competitors have to build from scratch.
How we sell it
Datics has run as a software factory for years: repeatable delivery pods, fixed scopes and codebases we did not write. Every AI engagement here has a name, a duration and a defined end state.
A written recommendation on what to build first and why.
Free
A sequenced plan you can fund, staff and defend in a security review.
$12k - $18k
One AI capability your customers use inside the product, plus reusable foundations.
$35k - $50k
AI-Native Sprint, Unlock, Capability WavesSprint, unlock, then capability waves that repeat.
Establish the product, workflow and architecture baseline, prioritize the AI capability portfolio and define the first wave.
Change only the parts of the product the prioritized AI capabilities actually require. No speculative rewrites.
Ship capability into production wave by wave. Each wave reuses the foundations of the last, so later capabilities cost less.
Each capability wave repeats
For private equity and shareholders
Owners set the mandate. The work happens inside the portfolio product, measured on retention, pricing power and defensibility.
Let us make it real without rebuilding what already works.
