A missed phone call is a small event with a long tail. Someone has to notice it, understand what the caller wanted, find the right record and arrange another conversation. At a dental or veterinary practice, the distance between a ringing telephone and a booked appointment is a business problem. It is also a useful place to understand Datics AI.
- Datics adds AI to established software, working through existing data, permissions and integrations.
- Planning and evaluation start at $5,000; production gets a separate fixed quote.
- Its practical test: does the feature finish a useful piece of work?
In a published practice-communications engagement, Datics connected call intelligence to follow-up and booking workflows through existing practice-management integrations. Its documented example reports 311 recovered appointments across six locations, associated with more than $100,000 in recovered revenue. These are company-reported results from one example, rather than a forecast for the next customer.
The interesting part is the journey between those numbers. Recognizing a conversation leaves the scheduling work undone. Getting an appointment into the system requires context, access and cooperation with software already running the practice. Datics has chosen that middle distance as its territory.
The company learned to build the rest
Brothers Umair Majeed and Rana Umar Majeed founded Datics in 2018, with roots in AI and data science. Today, its headquarters is in Austin and its engineering center is in Lahore. Umair leads the business; Umar leads technical strategy. Their combined brief runs from choosing a useful feature to getting it through production.
In a 2022 conversation with Muzamil Hasan, Umair described an early Upwork engagement at $40 an hour. A request for one engineer became a request for more. He also described the limitation of serving clients only with AI and machine learning: customers needed complete products. The firm expanded into business analysis, design, development and quality assurance.
That history explains its current specialization. Clients brought application problems along with model problems. Datics accumulated the engineering needed to handle both. Its current pitch concentrates that experience on established vertical SaaS: industry software with customers, recurring revenue and a product team whose calendar is already full.
“We make established software intelligent.”
Datics AI / the company’s stated brief
Buy the decision before the build
Datics calls this “brownfield AI.” In construction, brownfield means working on a site with a past. Here, the past includes business rules, integrations and permissions. A feature must fit those accumulated decisions while giving users something useful enough to adopt.
The commercial model begins with a free, 30-minute Product Review. A Planning Sprint then costs $5,000 for two weeks. It identifies the first capability, maps dependencies and produces a delivery brief and fixed production quote. A team can keep the plan and execute it itself.
For customers who already have AI, the alternative is an AI Scorecard: the same price and duration, testing one feature in staging against 50 client cases. Quality, data separation, misuse, response time and cost per user enter the assessment. The fee counts toward production, which is quoted separately. Later capabilities have their own quotes.
Plan the first capability or evaluate an existing one.
Production is a separate fixed quote.
A model cannot grant itself permission
Consider Datics’ regulated-investment case. The existing application used .NET and SQL Server. Datics added Python services for document intelligence and automation around that core, retaining the application and its AML/KYC controls. The architecture made room for new capabilities while keeping established operating rules in place.
A clinical-intake engagement tackled a different gap. An adaptive GPT-powered intake prototype already generated structured summaries. Datics helped surround it with clinic isolation, role-based access, provider configuration, onboarding and Stripe billing. The question had become whether multiple clinics could operate it as a product.
These examples show where the difficulty moves after a promising demo. An answer has to belong to the right customer, reach the right workflow and respect the right authority. Datics’ technical brief includes APIs, agent behavior, evaluation, cloud deployment and human approvals. Each is part of delivering the feature.
Bring the relevant record and workflow into view. A model needs the product’s context to act usefully.
A simplified explanation of the engineering approach.The classroom comes with strings attached
EdTech is a current focus. Datics presents modules for advising and application intake, conversational skills practice, syllabus and course drafting, learner support, and questions over enrollment data. Their names are Pathly, Rehearsly, Syllably, Docently and Cohortly. They are designed to appear inside a customer’s platform, under that customer’s name.
The Northstar institution used to demonstrate them is fictional. For a suitable module, a three-week Build Sprint costs $10,000 and targets a working outcome in staging. Production remains separately scoped. Client-specific code lives in the client repository; Datics retains and licenses the reusable engine underneath. Ownership deserves a careful contract conversation.
That puts Datics between familiar buying options. An internal engineering team brings product knowledge but may lack spare capacity. A general consultancy can supply development breadth. A point tool supplies a ready-made feature. Datics sells responsibility for making the AI and the surrounding application work together, within the client’s own product.
For education vendors, the economics matter as much as the interface. Customers may pay per learner while model usage creates costs per interaction. Datics includes cost-to-serve analysis and packaging recommendations in its EdTech offer. A useful feature needs a price that survives actual use.
One useful change at a time
In November 2025, Devsinc acquired Datics AI. The announcement named Umair SVP Growth and Umar SVP AI Enablement & Transformation. Datics described the benefit as access to enterprise clients across four continents and infrastructure for larger delivery. Its AI expertise gained a wider commercial route.
For buyers, the transferable idea is smaller: select one workflow, agree how success will be measured, and build only the access and integration it needs. Reuse those foundations for the next capability. This approach assumes a real product and client participation. Datics explicitly places greenfield AI startups and standalone chatbot projects outside its current core fit.
It is an unglamorous selection criterion, and a useful one. Start with the work somebody needs finished. At the practice desk, that might mean the appointment finally makes it onto the calendar.