Retention
Make high-value functionality easier for customers to discover and use inside existing workflows.
For private equity & software investors
Make AI part of the value-creation plan.
AI creates value when it improves the product customers already pay for. We work with portfolio company product and engineering teams to turn sponsor-level AI priorities into production capabilities that can support retention, expansion, operating leverage and defensibility — without forcing a platform rewrite.
- 2018
- AI & data science roots
- Brownfield AI
- Built for established software already in market
- Production delivery
- AI shipped inside live products, workflows and enterprise constraints
Value-creation levers
Retention
Expansion
Defensibility
Operational leverage
ROI and payback
Prioritized on business value, product feasibility and path to production.
Investment case
Where AI can move the investment case
We work backward from the outcomes ownership cares about — revenue, retention, operating leverage, defensibility and payback — not from a technology wishlist.
Expansion
Create intelligent capabilities that increase product value and may support premium functionality or new SKUs.
Defensibility
Combine domain workflows, product logic and customer context in ways generic AI products cannot easily reproduce.
Operational leverage
Automate high-friction work inside the product rather than adding service overhead around it.
ROI and payback
Prioritize AI capabilities where the expected commercial or operating benefit can be measured against the cost, risk and time required to ship.
Value x Feasibility x Payback
Not every AI capability deserves investment. We prioritize the ones with a credible path to production and payback.
Value
How much could the capability affect retention, expansion, pricing power, cost-to-serve and customer adoption?
Feasibility
Can the existing architecture, data, workflows, integrations and permissions support it without disproportionate modernization?
Payback
What is the estimated effort, cost and time to production, and what measurable business outcome would need to justify that investment?
The real constraint
The AI mandate is usually clear. The product is where execution gets hard.
Nothing here is a failure of intent. It is the normal operating reality of a mature vertical SaaS business.
- Management understands the AI agenda, but priorities compete for the same engineers.
- The architecture is mature, so meaningful AI needs product work before model work.
- The roadmap is already committed to customer and operational priorities.
- The internal team has strong domain engineers and limited AI product experience.
- Experiments get built, demo well and never clear security or procurement.
Our job is to translate an ownership-level AI priority into a product plan the portfolio company’s product and engineering leaders can execute — and into capabilities customers actually use.
Why sponsors use us
We are built for the products that make AI hard.
Mature architecture, accumulated business logic, integrations, permissions and customer constraints are the normal starting conditions in vertical SaaS. They are also where much of the product's defensibility lives — and where meaningful AI has to work.
Realistic assessment
We separate the capabilities that can move now from the dependencies that need to be cleared first.
Production bias
The measure is a capability customers use, not a proof of concept in a board deck.
We work with the team, not around it
The portfolio company keeps ownership of the product. We add the applied AI, product and production engineering depth required to ship the capability.
How the work gets delivered
From sponsor priority to production capability
The delivery model is the same one we use across Datics: define the path, clear the dependencies, then ship and improve in Capability Waves.
AI-Native Sprint
Define the path.
Understand the product, prioritize the right AI capabilities, identify dependencies and define what it will take to ship the first wave.
What the delivery model produces
- Prioritized AI capability portfolio
- Value, feasibility and dependency view
- Defined first production capability
- Reusable foundations and lessons for subsequent waves
Portfolio multiplier
Different products. A repeatable AI product playbook.
Every portfolio company has a different codebase, architecture and operating context. What can compound across the portfolio is the judgment: opportunity assessment, governance patterns, permission models, evaluation methods, deployment lessons and delivery practices. We adapt those patterns company by company rather than starting from zero each time.
Assessment framework
The same structured read on AI opportunity and architecture readiness, applied company by company.
Governance and security patterns
Tenancy, permissions, approvals and audit patterns refined across engagements and adapted to each company.
Agent and evaluation practices
Harness design, evaluation and observability practices that transfer even when the stacks do not.
Delivery playbooks
Production patterns and implementation lessons that can shorten the path, while each company's own security and architecture requirements are still respected.
How sponsors engage us
Two ways to start. One delivery model underneath.
Start with one portfolio company, or engage Datics across a selected group of assets. In both cases, each company keeps its own product-specific path while relevant judgment and patterns compound across the portfolio.
Start with one asset
Portfolio Company Engagement
Work with one portfolio company to identify the highest-value AI opportunities, define the path, clear the required dependencies and ship the first production capability.
Outcome
A prioritized AI plan and a production path for one portfolio company.
Expand across selected assets
Portfolio AI Program
Work across multiple portfolio companies where AI is part of the value-creation agenda. Each company gets its own product-specific path, while relevant governance patterns, evaluation methods and delivery lessons carry forward.
Outcome
A repeatable portfolio-level approach to AI delivery without forcing identical implementations.
Both sponsor engagement models use the same Datics delivery path: AI-Native Sprint → Targeted Unlock → Capability Wave.
See the full delivery modelSelected work
Production AI inside established software
Examples of working inside live products with existing users, workflows, integrations and operating constraints.
Financial Advisory · Investment & Portfolio Management Platform
AI-assisted due diligence inside an investment administration platform
AI-assisted fund onboarding, due diligence and document intelligence inside an investment administration platform.
- Brownfield
- Agents
- Document intelligence
Dental & Veterinary · Practice Communications SaaS
AI that acts inside a live healthcare workflow
AI communication and booking inside an established dental and veterinary practice platform.
- Brownfield
- Agents
- HIPAA-aligned
Financial Services · Investment Operations Software
Production AI added around an existing .NET core
Production AI and automation added around a regulated .NET investment platform instead of rebuilding the core.
- Brownfield
- Regulated
- AWS
Healthcare · Clinical Intake SaaS
Working AI prototype → production multi-clinic SaaS
A working AI intake prototype evolved into a clinic-isolated, role-aware production SaaS.
- Healthcare
- Production AI
- Multi-tenant
Experience with mature software assets
Brownfield is our standing operating environment: software we did not write, customers we cannot disrupt, workflows that matter, integrations that already exist, and production constraints that have to be respected.
Reviewing a vertical SaaS asset?
Start with one portfolio company. We identify the highest-value AI opportunities, the product changes required to ship them, and where the effort is most likely to create meaningful customer and business impact.