# Datics AI — full context

Site: https://www.datics.ai
Contact: business@datics.ai
Entry point: a 30-minute AI product review.

## Summary

Datics AI helps established vertical B2B SaaS companies become AI-native by bringing
production AI into the product they already operate — without forcing a rebuild.
Datics specializes in Brownfield AI: AI shipped into mature, integrated,
permission-heavy software with paying customers.

Primary headline: Make your SaaS AI-native without starting over.

## Who Datics works with

Established vertical B2B SaaS companies with paying customers, mature workflows,
business logic, integrations, permissions and multi-tenancy, accumulated product and
data complexity, and internal product and engineering teams.

Primary buyers: CTO, CPO, VP Engineering and senior product or engineering leadership.
Also: private equity operating partners responsible for portfolio SaaS assets.

Datics is not a generic AI development agency, a cloud consultancy, a staff
augmentation company, or a greenfield AI startup studio. Cloud, data, application
architecture, DevOps and platform engineering are supporting capabilities used when
the AI roadmap requires them.

## The problems Datics owns

1. The AI roadmap keeps stalling.
   Teams know the product needs to become AI-native but cannot confidently decide what
   to build first, how it fits the product, what architecture must change, or how to
   avoid a broad rewrite. Datics answer: the AI-Native Sprint.

2. The AI produces intelligence but does not remove work.
   AI summarizes, recommends or scores, yet users still perform the workflow by hand.
   Datics answer: agentic product capabilities connected to the product's APIs,
   workflows, integrations, permissions, data and business rules, so AI performs work
   rather than describing it.

3. The demo works; production stops it.
   Prototypes meet multi-tenancy, authorization, auditability, security, data access,
   evaluation, observability, latency, customer-cloud constraints, integrations and
   failure handling. Datics answer: production AI foundations — context, tools,
   permissions, evaluations, governance and architecture around the model.

## Delivery method

AI-Native Sprint → Unlock → Capability Waves (repeating).

AI-Native Sprint: what should we build, and what will it take? Understand product,
users, workflows, architecture, data, integrations, tenancy, permissions, security and
current AI initiatives. Prioritize capabilities using value, feasibility and payback.
Outputs: capability portfolio, priorities, architecture implications, dependencies,
risks, required unlock work and the first production wave. Fast and opinionated, not
open-ended consulting.

Unlock: change only what the prioritized AI capability requires. Typical changes span
APIs, data access, authorization, service boundaries, eventing, retrieval and context,
model access, observability, evaluation infrastructure and deployment boundaries. Never
broad modernization for its own sake.

Capability Waves: ship meaningful AI capabilities into the existing product, running
build → measure → improve → reuse → next wave. Each wave leaves behind reusable
context, tools, permissions, evaluations, governance and infrastructure, so the next
capability starts from a stronger foundation.

## Solution categories

1. AI Product Experiences — embedded copilots, natural-language interaction, generative
   UI, intelligent discovery and search, decision support, contextual assistance.
2. Agentic Workflows — deep agents and multi-step agents, tool and API calling, workflow
   automation, cross-system actions, human-in-the-loop, approvals, supervised agents.
3. AI Product Foundation — context, tools, permissions, memory and state, evaluation,
   observability, governance. Datics' agent harness lives in this layer.
4. Targeted Product Modernization — application architecture, APIs, data engineering,
   integrations, identity and permissions, cloud and infrastructure changes required by
   the AI roadmap. Modernize only what the AI roadmap requires.

## Platforms and engineering context

Delivery across AWS, Microsoft Azure and Google Cloud, including cloud-to-cloud
migration and inference cost work. Model platforms include AWS Bedrock, Azure AI and
Vertex AI. Agent engineering uses LangChain, LangGraph and CopilotKit, including deep
agents with human-in-the-loop supervision; Databricks is used for governed AI and agent
workflows; Cursor is part of the engineering toolchain. Datics' agent harness pattern
supplies the context, tools, permissions and evaluation around generative AI models so
copilots and agents can run safely inside AI-native SaaS products.

## Pages

- https://www.datics.ai/ — positioning, problems, product layers, proof
- https://www.datics.ai/what-we-do — solution categories and agent harness engineering
- https://www.datics.ai/guidely — in-product assistant reference build
- https://www.datics.ai/how-we-work — Sprint, Unlock and Capability Waves in detail
- https://www.datics.ai/cloud-modernization — cloud and inference economics
- https://www.datics.ai/work — case studies
- https://www.datics.ai/for-pe — private equity value creation
- https://www.datics.ai/industries/edtech — AI product modernization for established EdTech software: AI agents, copilots and generative UI inside admissions, assessment, financial aid and campus operations workflows, without rebuilding the existing platform. Education-ready controls including FERPA and COPPA-aware data handling. Entry point: a 30-minute EdTech product review.
- https://www.datics.ai/about — background, partnerships and credentials
- https://www.datics.ai/insights — buyer-question answer pages

## Insights

- https://www.datics.ai/insights/why-ai-agents-fail-in-production — Rana Umar Majeed on production agent failure patterns, CAP trade-offs, the CARE design lens, deterministic control boundaries and the Read-to-Execute capability ladder
- https://www.datics.ai/insights/add-ai-agents-to-existing-saas-without-rebuilding
- https://www.datics.ai/insights/vibe-coding-not-enough-for-mature-saas
- https://www.datics.ai/insights/how-much-does-production-ai-cost-in-saas
- https://www.datics.ai/insights/bedrock-vs-azure-ai-vs-vertex-ai-for-existing-saas
- https://www.datics.ai/insights/when-should-an-ai-workload-move-between-clouds
- https://www.datics.ai/insights/why-ai-features-damage-saas-gross-margins
- https://www.datics.ai/insights/reduce-ai-infrastructure-cost-in-production

## Case studies

- https://www.datics.ai/work/peerlogic — AI communication intelligence inside a live practice platform
- https://www.datics.ai/work/dawa — production AI around a regulated investment platform
- https://www.datics.ai/work/mira — intelligent intake taken to production multi-clinic SaaS
- https://www.datics.ai/work/stealth-dealership-intelligence — AI customer-intelligence layer for dealerships
- https://www.datics.ai/work/dealership-toolkit-portal — modernizing dealership SaaS without rebuilding it
- https://www.datics.ai/work/amove-cloud-management — reliability to AI knowledge retrieval in a live cloud product
- https://www.datics.ai/work/broomy-cleanhaus — AI field-operations layer on a property-service workflow
- https://www.datics.ai/work/syla-ai — dealership conversations turned into qualified leads
- https://www.datics.ai/work/neuroreef — AI clinical documentation in the dentist workflow
- https://www.datics.ai/work/teg-enterprise-modeling — AI process modeling across six capability phases
- https://www.datics.ai/work/captura — data foundation for a high-volume photography platform
- https://www.datics.ai/work/evolve — replacing a legacy storefront layer in a fulfillment platform
- https://www.datics.ai/work/mylotspy — searchable competitor inventory intelligence
- https://www.datics.ai/work/drive-ott — one surface for campaigns across fragmented OTT channels
- https://www.datics.ai/work/gce — verifiable AI with provenance, truth tiers and audit gates
- https://www.datics.ai/work/titotruth — structured semantic units for claim verification
- https://www.datics.ai/work/transcript-us — discoverable court transcripts without pre-purchase exposure
- https://www.datics.ai/work/max-360 — investor data synchronized in a relationship platform
- https://www.datics.ai/work/cosmic-marketverse — market data as AI decision support
- https://www.datics.ai/work/cosmic-platform — conversational KPIs with organizational context
- https://www.datics.ai/work/sentrac — social signals as AI campaign intelligence
- https://www.datics.ai/work/threatmap — threat, incident and response data in one picture
- https://www.datics.ai/work/ai-roleplay-assistant — repeatable sales practice with AI roleplay
- https://www.datics.ai/work/nenodoc-lexicon — extraction, cited RAG and AI writing, self-hosted
- https://www.datics.ai/work/selectus — matching, immigration guidance and career development
- https://www.datics.ai/work/peret — research-grounded AI forecasts for prediction markets
- https://www.datics.ai/work/ftrv — one organizational platform for employee operations
- https://www.datics.ai/work/upgrade-and-save — parts, service campaigns and e-commerce connected
- https://www.datics.ai/work/social-opps — social advertising synchronized with live inventory
- https://www.datics.ai/work/funtown-smat — social content from scheduling to automated publishing