Datics AI
See if a Sprint fits

AI-Native Sprint · 2 weeks · Fixed scope · $5,000

Know what AI to ship first, in 2 weeks.

We work inside the SaaS product you already run to identify the highest-value AI capability, map what it requires from your architecture, data, permissions and integrations, and define the first production Capability Wave, without a speculative rebuild.

See if a Sprint fitsSee a sample Sprint Pack
  • One existing product
  • No speculative rewrite
  • Security and tenancy considered up front

Before → Sprint → What ships first

2 weeks

Where teams start

  • We need an AI roadmap
  • Prototype will not ship
  • Permissions / tenancy
  • Integrations
  • Security review
  • What ships first?

Inside the Sprint

Start with the product as it exists.

What you leave with

  • Product & Dependency Map
  • AI Capability Portfolio
  • Production Readiness Plan
  • First Capability Wave Brief

First Capability Wave

Inspection Copilot

Illustrative example, not client work.

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

Automotive & 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 study
All case studies

Examples of Datics production and brownfield AI work. These were not necessarily AI-Native Sprint engagements.

Why teams start here

You do not need another AI brainstorm. You need a plan product and engineering can ship.

Established SaaS rarely lacks AI ideas. It lacks agreement on which capabilities matter first, whether the existing product can support them, and what must change before they can survive production.

  • Customers are asking for an AI roadmap.

    The answer is still a list of ideas rather than a sequence.

  • A prototype works, but will not ship.

    Tenancy, permissions, integrations, evaluation or deployment are now the real blockers.

  • AI keeps losing to the committed roadmap.

    The team needs a smaller, defensible first wave instead of a parallel transformation program.

  • Architecture is getting in the way.

    Nobody wants a rewrite, but AI needs access to context, tools, data and permissions the product was not designed to expose.

The Sprint turns those constraints into a buildable sequence.

The Sprint Pack

This is what two weeks produces.

You receive a working set of decision and delivery artifacts: a plan product, engineering and security can take forward, not a slide deck.

01 · Artifact

Product lead

AI Capability Portfolio

What it answers: Which AI capability should we fund first?

  • Capabilities ranked on value, feasibility, dependencies and payback
  • The capability that goes first

Illustrative extract

CapabilityValueFeasibilitySequence
Inspection CopilotHighHighFirst Wave
Escalation NoticesHighMediumAfter Unlock
NL ReportingMediumHighWave 2
View in the Sample Sprint Pack

02 · Artifact

Technical lead

Product & Dependency Map

What it answers: What does the current product already support, and where are the blockers?

  • Product, workflow, architecture and integration baseline
  • The context, data, APIs and permissions AI needs to reach

Illustrative extract

  1. Inspection workflow
  2. Product APIs / data
  3. Permissions / integrations
  4. AI capability
  • Dependency: inspection records exposed read-only via internal API.
  • Dependency: supervisor role required before any escalation write.
View in the Sample Sprint Pack

03 · Artifact

Technical lead

Production Readiness Plan

What it answers: What has to change before this can survive production?

  • Security, tenancy, permissions, governance, evaluation and observability status
  • The minimum unlock work required, not speculative modernization

Illustrative extract

  • Inspection read APIsReady
  • Authorization / tenancyNeeds change
  • Retrieval indexNeeds change
  • Evaluation harnessRisk
  • Scheduling write accessRisk
View in the Sample Sprint Pack

04 · Artifact

Product + engineering

First Capability Wave Brief

What it answers: What exactly does product and engineering build next?

  • Product flows, interaction design and implementation sequence
  • Success and evaluation criteria for the wave

Illustrative extract

Capability
Inspection Copilot
User outcome
Overdue inspections triaged and escalations drafted in one pass.
Product surface
Inspection queue, inline assistant panel.
Dependencies
Read APIs, role checks, retrieval over prior findings.
Human control
Supervisor approves every escalation before it sends.
Success criteria
Triage time per queue, approval rate, escalation accuracy.
View in the Sample Sprint Pack

Done means

A sequenced plan product, engineering and security can take forward.

See a sample Sprint PackSee if a Sprint fits

Inside the Sprint

Baseline → Prioritize → De-risk → Sequence.

Four stages of work inside two weeks, run with the people who already know the product.

  1. A · Baseline

    Read the product as it exists: workflows, architecture, data, tenancy and permissions.

  2. B · Prioritize

    Rank AI opportunities with your team on customer value, feasibility and expected payback.

  3. C · De-risk

    Test the leading candidates against production reality: APIs, authorization, evaluation, observability, deployment.

  4. D · Sequence

    Settle the first capability, the unlock work it depends on, and the order the rest follows in.

What we need from your team

  • Access to relevant product and technical context
  • Existing documentation, repository or system access where appropriate
  • A small number of working sessions with people who know the product and architecture

Best fit

Built for SaaS products that already have something to protect.

The Sprint assumes an existing product, real customers and real constraints. That is the whole point.

  • Established vertical B2B SaaS with live customers and recurring revenue
  • Workflow-heavy product with domain logic, integrations, roles and permissions
  • Internal product/engineering team that knows the product but has a full roadmap
  • AI has become a business priority due to customers, competitors, board or investor pressure, or a prototype that has stalled

Not a fit

Greenfield AI startups, standalone chatbot projects, generic AI workshops, or pure staff augmentation.

Standard Sprint boundary

One established SaaS product. The Sprint is a decision and production-planning engagement. It is not implementation, penetration testing, formal certification or an exhaustive codebase audit. It identifies and sequences the minimum Targeted Unlock work the prioritized capability requires. Complex multi-product estates are scoped separately.

The team behind the Sprint

A cross-functional team works the problem from product decision to production reality.

Each Sprint brings together product, architecture, applied AI, data, UX and production engineering expertise according to what your product requires.

The Sprint draws from a senior cross-functional pod. Specialists participate where their expertise is required.

Core pod · on every Sprint

  • Product Strategy

    • Workflow understanding
    • Customer/business value
    • Capability portfolio and prioritization

  • Solution Architecture

    • Existing architecture and service boundaries
    • Integrations and technical dependencies
    • What can be reused vs what must change

  • Applied AI

    • Model/agent approach
    • Retrieval/context/evaluation
    • Reliability and technical feasibility

Specialists · brought in where the product requires it

  • Data Engineering

    Data availability, pipelines and access; data dependencies for the chosen capability.

  • Product UX / AI Experience

    Human-in-the-loop flows, approvals and supervision, fit with the existing workflow.

  • Cloud & Security Engineering

    Tenancy, permissions, deployment boundaries, observability and production constraints.

How the Sprint Pack gets produced

  • Decided togetherProduct strategy and applied AI set which capabilities matter and in what order.
  • Verified against the productArchitecture, data and security check every recommendation against what your product can actually support.
  • Written for deliveryUX and engineering turn the chosen capability into a brief your team can build from.

CTO Technical Review

Before handoff, the technical recommendations, dependency map and First Capability Wave pass through a final architecture and production-readiness review.

Final technical review: Rana Umar Majeed, Co-founder & CTO.

  1. Cross-functional Sprint Pod
  2. CTO Technical Review
  3. Sprint Pack
  4. Client Handoff

The Sprint is the start, not the dependency

Your team can execute the plan. Or we can help ship it.

The Sprint stands on its own. It does not obligate you to hire Datics for implementation.

  1. 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.

    • Product baseline
    • Capability priorities
    • Delivery requirements
    • First wave

Why Datics

Brownfield AI is our specialty, not a side capability.

Datics started in AI and data science in 2018 and grew into SaaS product engineering. The work has stayed inside established software: capabilities shipped into products that already had customers, releases and support obligations, without a rebuild.

Brownfield by specialization

We work inside products that already carry years of customers, workflows, integrations, permissions, contracts and business rules. Not an empty repository, and not a rewrite.

AI and data from the beginning

Datics began in AI and data science in 2018 and grew into SaaS product engineering. Generative AI landed where those two capabilities already met, rather than being a post-GenAI pivot.

We can change what is underneath the AI

Application architecture, APIs, data access, permissions and production infrastructure, as well as models and orchestration. Most AI capability stalls on the product beneath it, which is the part we can actually change.

In AI and data since
2018
AI builds inside products already live
8

Ecosystem & engineering credibility

AWS

Select Tier Partner

Partner

CopilotKit

Agent user interface

Contributor

LangGraph

Production agent engineering

Engineering depth

Databricks

Governed AI & agent workflows

Engineering depth

Microsoft Azure

Production AI delivery

Delivery platform

Google Cloud

Production AI delivery

Delivery platform

Regulated, multi-tenant and permissioned environments included, with existing cores retained rather than rewritten, as in the Dawa .NET platform.

See our work

What it costs

One product. Two weeks. Fixed scope.

A bounded commercial container. The technical findings vary by product; the process and the outputs do not.

AI-Native Sprint

$5,000

2-week engagement · Fixed scope · One established product

  • Complete Sprint Pack
  • Cross-functional Sprint pod
  • CTO technical review
  • Defined First Capability Wave
  • No implementation obligation
See if a Sprint fitsSee a sample Sprint Pack

Complex multi-product or multi-business-unit environments are scoped separately before work starts.

Questions

Before you book

Is this just an AI strategy workshop?
No. It is a fixed-scope product and engineering engagement grounded in the product, architecture, workflows, data and permission model you already run. You leave with concrete artifacts and a defined first Capability Wave, not a summary of ideas.
Why is the Sprint fixed scope if every product is different?
The technical findings vary by product. The decision process and the outputs do not. Keeping the engagement to one established product is what makes two weeks and a fixed price honest.
What do you need from our team?
Relevant product and technical context: existing documentation, architecture and system access where appropriate, plus a small number of working sessions with the people who know the product, workflows and architecture.
What if we have multiple products or business units?
The standard Sprint covers one established SaaS product. Complex multi-product or multi-business-unit estates are scoped separately before any work starts.
What is not included?
No implementation, no exhaustive code audit, no formal security assessment, certification or penetration test, and no broad platform modernization. The Sprint identifies and sequences the minimum Targeted Unlock work the prioritized capability requires.
Do we have to rebuild our product first?
No. The point is to identify the minimum unlock work the prioritized capability actually requires, not to use AI as an excuse for a rewrite.
What if we already have an AI prototype?
Good. The Sprint determines what is keeping it from production (tenancy, permissions, integrations, evaluation, deployment, governance or product fit) and sequences the work to ship it.
What happens after the Sprint?
Your team can execute the plan, or Datics can continue into the required Targeted Unlock and Capability Wave. The Sprint does not obligate you to hire Datics for implementation.

Two weeks to turn “we need AI” into what ships first.

Bring the product you already have and the AI pressure you are feeling. We will use the first conversation to determine whether an AI-Native Sprint is the right next step.

See if a Sprint fits

30-minute conversation · No prep required · No pitch deck

See our work
See if a Sprint fits