Datics AI
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.

Book a 30-minute Product ReviewSee our work
  • 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

Three ways to start: Build Sprint · Planning Sprint · AI Scorecard

Building for another vertical? See where we have shipped AI ↓

learn.northstarcampus.com / practice / results

Northstar Workforce Academy · Practice results

AI scored
Learner · scenarioScoreFlag
D. Patel · Price objection3/6Skipped needs check
R. Silva · Price objection2/6No next step
T. Nguyen · Price objection3/6Rushed close

Northstar Coach

3 learners below target

  • 1 skipped needs check
  • 1 no next step

Coaching notes drafted from each call

To D. Patel: Ask about current coverage before presenting a plan.

Review feedbackAssign practice

See AI for EdTech →

Illustrative example, not client work.

Your assistant, your name, built inside your product.

AI shipped inside existing products

Peerlogic logoAMove logoDealership Toolkit logoIdeeza logoRVP Pricing Tool logo

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.

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 EdTech

  • Compliance & 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 work

We have also shipped AI and platforms into 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.

The four problems that stall AI in established vertical B2B SaaS: AI keeps losing to the roadmap, the product was not built for AI to use, AI can answer but cannot do the work, and the demo cannot survive production. For each one, what we do about it.

  1. 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.

  2. The product was not 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, and leave the rest of the product as it is.

  3. AI can answer, but it cannot 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.

  4. The demo cannot 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.

See how we fix each one

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

MeridianInspectionsR. Mehta

Inspection queue

4 open · Northeast

RefSiteStatus
INS-8841Harbor Point TerminalEscalate
INS-8846Bridgeport Depot 4Escalate
INS-8852Lakeview ProcessingReview
INS-8860Cedar Falls PlantScheduled

Manual filtering, manual selection, manual escalation. Eight clicks per decision.

Try the full working demo

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.

  1. 01

    AI product experiences

    AI appears in the screens and workflows users already use.

  2. 02

    AI-ready architecture

    The tools, data and permissions AI is allowed to call.

  3. 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

  4. 04

    Production and governance

    Tenancy, audit, rollout and monitoring once it is live.

See how it is built

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

  1. 01Add AI capabilities one at a timeShip AI into the product one capability at a time, without a platform rewrite.
  2. 02Remove the architecture blockersAI that can safely use your product's data and actions, on the platform you already have.
  3. 03Build agents people can superviseGive users visibility, interruption, correction and approval where it matters.
  4. 04Turn AI output into actionAI that finishes the task inside the workflow, not a suggestion your users still have to act on.
  5. 05Deploy AI enterprise customers can approveEnterprise deals that keep moving, because data, hosting and access already fit what your customers' reviews ask for.
  6. 06Define the first capability to shipTurn a broad AI mandate into a prioritized first capability product and engineering can actually build.

Services

  1. 01

    Pick a start

    Build the outcome you already know you want with a Build Sprint, plan something new with a Planning Sprint, or test what you have built with an AI Scorecard. Each has a fixed price and length, shown below.

  2. 02

    Production, at a fixed quote

    Then we build and release it in waves, inside your codebase and reviewed by your team, each at a fixed quote with a two-week warranty, changing only what the capabilities need.

  3. 03

    Add more, only if you want it

    Later waves ship one at a time, each at its own fixed quote. Nothing ongoing unless you ask, and you can stop after any start.

Your fee counts toward production. Not sure which fits? Answer four questions.

Want our engineer inside your team? An Outcome Sprint ($2,500, one week). Your product in Claude and ChatGPT? A Connector Starter ($2,500).

Each capability builds on the last

  1. Ship one capability.
  2. Reliability, adoption, impact.
  3. Tune behavior and workflow.
  4. Carry context forward.
  5. Back to Build, not to zero.

Build: We ship one capability into your live product, inside your codebase.

See the engagement modelExplore all solutions

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 study

Healthcare · 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 study

Financial 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 study
All case studies

What clients say

“CleanHaus has been moving quickly to deploy our app and Broomy AI Agent with the key assistance of the Datics team. We couldn't have reached this point without their excellent bench of developers.”
Nat ParkerNat ParkerCEO and Co-Founder, CleanHaus

5.0 average client rating

Why Datics

AI inside existing products is our specialty, not a side job.

We work on both the intelligence and the software it depends on.

Brownfield is our focus

Most of our work is inside products that already have customers, integrations, permissions and business rules, changing only what the AI needs.

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 is underneath the AI

Most AI stalls on the product beneath it: architecture, APIs, data access and infrastructure. That is the part we change.

In AI and data since
2018
Featured AI case studies
57

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

Insights

Answers to what SaaS leaders ask before adding AI

Written by the people who build it: how we approach AI in a product you already run.

All 81 answers

Automotive and dealerships

AI for car dealerships: where it pays in dealer software

AI for car dealerships pays in sales, BDC follow-up and service booking first, grounded in live inventory and wired into each dealer's CRM and DMS.

Umair Majeed · Nov 8, 2026

AI-native

Which workflow should you make AI-native first?

A simple five-factor score (value, frequency, data readiness, risk and effort) for choosing the first workflow to make AI native in an established product.

Umair Majeed · Nov 7, 2026

Brownfield AI

AI implementation challenges: lessons from 50+ projects

The AI implementation challenges behind 53 AI projects since 2018: integration, data, permissions, approvals and testing, and what to settle first.

Umair Majeed · Nov 4, 2026

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.

Book a 30-minute Product Reviewor email [email protected]
Book a 30-minute Product Review