Brownfield is all we do
We work inside products that already have customers, integrations, permissions and business rules. No empty repos, no rewrites.
For established vertical B2B SaaS
Make your SaaS AI-native without starting over.
Established vertical SaaS comes with years of workflows, data, integrations and permissions. We make AI work with those realities so it can reach production without derailing the product roadmap.
- Built inside the product you already run, under your name
- Live in production, not stuck in a pilot
- Runs in your cloud, on your model, by default
Two ways to start, $5,000 each: Planning Sprint · AI Scorecard
Building for another vertical? See where we've shipped AI ↓
learn.northstarcampus.com / practice / results
Northstar Workforce Academy · Practice results
AI scoredLearner · scenarioScoreFlag
Illustrative example, not client work.
Your assistant, your name, built inside your product.
AI shipped inside existing products
Best fit
Built for SaaS products that already have something to protect.
You have paying customers, a committed roadmap and pressure to ship AI. The challenge is doing it without putting the product people already depend on at risk.
Established product
Live customers, recurring revenue and years of product decisions behind it.
Workflow-heavy software
Domain logic, integrations, roles and permissions that AI has to work through.
A product team already at capacity
Your team knows the product, but the roadmap is full and production AI is new ground.
AI is now a business priority
Customers are asking, competitors are moving, or a prototype never reached the product.
Probably not the right fit: greenfield AI startups or standalone chatbot projects.
Industries
Where we go deepest.
AI built inside the product you already run, shown on working demos and shipped under your name.
AI for EdTech
AI inside the platform your customers already use.
Five AI modules by Datics AI
Explore AI for EdTechCompliance & field ops
An AI operations layer for a property field-service team, plus AI document and compliance workflows around a regulated investment platform.
See the workHealthcare practices
AI clinical notes in the dentist's appointment flow, call intelligence for dental and veterinary practices, and adaptive patient intake for clinics.
See the workDealerships & automotive
Inventory-grounded sales AI, unified customer intelligence and competitor-inventory search for dealership software.
See the work
We've also shipped AI and platforms into financial services, legal, enterprise software and beyond. See all case studies →
The problems we solve
The model is not the bottleneck. The product around it is.
Four places AI initiatives stall inside established SaaS products.
AI keeps losing to the roadmap.
Where it stalls
AI is a priority, but every initiative competes with an already committed roadmap.
What we do
Prioritize the capability portfolio and sequence what ships first.
The product wasn't built for AI to use.
Where it stalls
Your workflows, data, APIs and permissions were built for users and application code — not agents.
What we do
Expose the context, tools and boundaries AI needs without rebuilding the product.
AI can answer, but it can't do the work.
Where it stalls
The prototype produces intelligence, but customers still have to complete the workflow themselves.
What we do
Connect AI to workflows, APIs, permissions and approvals so it can act.
The demo can't survive production.
Where it stalls
Tenancy, security, audit, evaluation and deployment turn a working demo into a production problem.
What we do
Design production controls in from the start.
What AI-native looks like
AI inside the workflow, not a chatbot beside it.
Useful AI needs the same context, tools, permissions and approval rules the product already relies on.
The product as it ships today: every decision reached by filtering, reading and clicking.
app.meridiancompliance.com / inspections / queue
MeridianDashboardInspectionsSitesFindingsR. Mehta
Inspection queue
4 open · Northeast
SearchRegionStatusInspectorDue date
RefSiteInspectorDueStatus
INS-8841Harbor Point TerminalR. AlvarezOverdue 6dEscalate
INS-8846Bridgeport Depot 4K. ItoOverdue 3dEscalate
INS-8852Lakeview ProcessingM. BooneOverdue 1dReview
INS-8860Cedar Falls PlantD. NkemeluIn 2dScheduled
Manual filtering, manual selection, manual escalation. Eight clicks per decision.
What changes inside the product
Change only what the AI capability actually requires.
Each solution may touch one layer or several. We only change the parts needed to make it work safely in production.
- 01
AI product experiences
AI appears in the screens and workflows users already use.
- 02
AI-ready architecture
The tools, data and permissions AI is allowed to call.
- 03
Agent harness
Context, memory, approvals and evaluation around the model.
The AI action path:
User intent: Understand what the user is trying to accomplish, not just the words they typed.
Where people stay in controlSelected step
- 04
Production and governance
Tenancy, audit, rollout and monitoring once it is live.
How we work
A practical path from AI mandate to production.
Pick a start, go live at a fixed quote, add more only if you want it.
What we help with
- 01Add AI capabilities one at a timeShip AI into the product one capability at a time, without a platform rewrite.
- 02Remove the architecture blockersAI that can safely use your product's data and actions, on the platform you already have.
- 03Build agents people can superviseGive users visibility, interruption, correction and approval where it matters.
- 04Turn AI output into actionAI that finishes the task inside the workflow, not a suggestion your users still have to act on.
- 05Deploy AI enterprise customers can approveEnterprise deals that keep moving, because data, hosting and access already fit what your customers' reviews ask for.
- 06Define the first capability to shipTurn a broad AI mandate into a prioritized first capability product and engineering can actually build.
- 01
Pick a start
Plan something new with a Planning Sprint, or test what you've already built with an AI Scorecard. Both are two weeks at a fixed price, shown below.
- 02
Production, at a fixed quote
We take what your start planned or tested live, inside your codebase and reviewed by your team, changing only what the capability needs.
- 03
Add more, only if you want it
Later capabilities ship one at a time, each at its own fixed quote. Nothing ongoing unless you ask, and you can stop after either start.
- Planning Sprint$5,000 · two weeksFor something new, or to plan before you build.
- AI Scorecard$5,000 · two weeksIf you've already built or tried AI. Tests it on 50 of your own cases and ranks the fixes.
Your fee counts toward production.
Each capability builds on the last
- Ship one capability.
- Reliability, adoption, impact.
- Tune behavior and workflow.
- Carry context forward.
- Back to Build, not to zero.
Build: We ship one capability into your live product, inside your codebase.
Proof
AI shipped into software customers already pay for
These were live products with real users, existing workflows and constraints we could not ignore.
Dental & Veterinary · Practice Communications SaaS
AI communication intelligence inside a live practice platform
AI communication and booking inside an established dental and veterinary practice platform.
BrownfieldAgentsHIPAA-aligned
Read the case studyHealthcare · Clinical Intake SaaS
Taking an intelligent intake prototype to a production multi-clinic SaaS
A working AI intake prototype evolved into a clinic-isolated, role-aware production SaaS.
HealthcareProduction AIMulti-tenant
Read the case studyFinancial Services · Investment Operations Software
Adding production AI around a regulated investment platform without replacing its .NET core
Production AI and automation added around a regulated .NET investment platform instead of rebuilding the core.
BrownfieldRegulatedAWS
Read the case studyAutomotive & Dealerships · Customer Intelligence Platform
Unifying fragmented dealership data into an AI customer-intelligence layer
Fragmented dealership CRM, customer and inventory data unified into an actionable AI intelligence layer.
DealershipsCRM integrationAI enrichment
Read the case studyWhat clients say
Datics has been an invaluable partner in custom software development, delivering high-quality solutions swiftly. Their services come highly recommended.
Richard DeLanceyCIO, Coast TechnologyPartnering with Datics AI transformed our projects. Their innovative solutions consistently exceeded our expectations, always delivering a step ahead.
Morran BitonCEO, IdeezaWhy Datics
AI inside existing products is our specialty, not a side job.
We work on both the intelligence and the software it depends on.
AI and product engineering, together
Our AI and data work predates generative AI and grew up alongside SaaS engineering. GenAI landed where those skills already met.
We fix what's underneath the AI
Most AI stalls on the product beneath it: architecture, APIs, data access and infrastructure. That's the part we change.
- In AI and data since
- 2018
- Featured AI case studies
- 18
Ecosystem & engineering credibility
AWS
Select Tier Partner
Partner
Anthropic
Partner · Claude models
Partner
CopilotKit
Agent user interface
Contributor
LangGraph
Production agent engineering
Engineering depth
Databricks
Governed AI & agent workflows
Engineering depth
Cursor
Agentic development environment
Engineering capability
Microsoft Azure
Production AI delivery
Delivery platform
Google Cloud
Production AI delivery
Delivery platform
That complexity is not just technical debt. It is also the domain knowledge, system access and customer context new AI-first entrants still have to build.
For private equity: you set the AI priority across the portfolio, we build it inside the product.See how we work with PE portfolios
Your product already works. Now make AI work inside it.
No charge. You leave knowing where AI fits in your product and which start makes sense.