Datics AI

What we do

What it takes to make mature software AI-native

These are not four unrelated service practices. They are four layers required to solve one problem: adding real intelligence to a product that already has customers.

Assess an AI opportunitySee how we work

Request path

  1. 1

    Intent

  2. 2

    Context

  3. 3

    Tools

  4. 4

    Permissions

  5. 5

    Approval

  6. 6

    Action

  7. 7

    Audit

Every AI action in a mature product travels this path before it touches customer data.

Use cases

Solutions we build inside a capability wave

Discovery, procurement and supervised agent adoption are product engineering problems before they are model problems. Here is what changes in the product.

01

Customers cannot reach the value.

Value your customers never find

Today

Customers churn over functionality they never discovered, while the support queue fills with tickets asking for features that already ship.

How we solve it

We build a natural-language layer into the existing product that reads intent and takes the user to the right feature or workflow, wired to your real permissions and data rather than a chat tool bolted to the side.

Support queue

Can we bulk-close inspections?ships
Is there an export?ships
Need a recurring scheduleships

In-product intent bar

close every passed inspection
Bulk close - 42 records
Scoped to your permissions
02

Security will not let it ship.

The procurement wall

Today

The AI feature works in the demo and stalls the moment procurement opens. Subprocessor, data-residency, permission and audit questions arrive at once, and nobody on the team has the answers.

How we solve it

We design tenancy, identity, permissions, approval gates and audit trails into the capability from week one, and deploy it inside your environment, so the security review has something real to evaluate instead of a demo with a compliance gap underneath it.

Security review

Demo environment
Data residencyblocked
Subprocessorsblocked
Permission modelblocked

Your environment

Deployed in your cloud
Data residencyanswered
Subprocessorsanswered
Permission modelanswered
03

Users do not trust it enough to adopt it.

Agents nobody trusts twice

Today

The first real agent ships, then adoption dies after two weeks. Users cannot see what it is doing, cannot stop it mid-task, and cannot correct it without starting again, so supervisors refuse to let it touch anything that cannot be undone.

How we solve it

We put a real supervision path in front of the agent: streamed steps the user can watch, an interrupt that actually stops the run, and an approval gate before the first irreversible action, all rendered inside the product. Some teams want a person approving every action, others want the agent to run and a person to review it. We support both.

Agent, unsupervised

Working...
No visible steps
Cannot stop or correct

Supervised run

Pulled 128 open findings
Drafted remediation plan
Approve before sendgate

The four layers

What has to be true before an agent can work inside your product

Four layers, one brownfield problem. Each one is work we do, and each one is a place an AI roadmap can stall.

01

AI Product Experiences

The part your customers see and judge. We build intelligence into the screens and workflows people already use, so the capability feels like the product rather than a chat window parked next to it.

  • Embedded copilots
  • Generative UI
  • Natural-language interaction
  • Product discovery
  • Agentic workflows
02

AI-ready Product Architecture

The changes a mature product needs before an agent can safely operate inside it. We expose the right tools, context, data access and permission boundaries, and we change only what the capability requires.

  • APIs and tools
  • Application context
  • Data access
  • Identity and permissions
  • Workflow modernization
  • Targeted brownfield architecture changes
03

Agent Harness & Intelligence

The engineering layer around the model, where reliability is won or lost. It is the largest of the four layers, and the one we open up in full further down this page.

  • Context
  • Tools
  • Memory
  • Orchestration
  • Approvals
  • Evaluation
04

Production & Governance

What turns a working prototype into something you can sell, support and pass a security review with. Tenancy, audit, deployment control and failure handling are designed in, not retrofitted after procurement asks.

  • Security boundaries
  • Tenant isolation
  • Audit trails
  • Deployment controls
  • Customer cloud
  • Self-hosted models
  • Air-gapped environments

We do not bolt AI onto the side. We prepare the product for AI, build the harness around the model, and make intelligence part of how the product works.

Layer 03, in detail

The agent harness, explained once

This is where we open up layer three. It is the engineering layer between a model and a product that already has customers: everything the agent is allowed to know, call, change and be held to.

  • Product context
  • Trusted tools
  • Permissions
  • Tenant boundaries
  • Business rules
  • Memory
  • Human approvals
  • Audit trails
  • Observability
  • Failure handling

Model and agent layer

  • LangGraph
  • AWS Strands
  • AG-UI
  • CrewAI
  • Azure AI
  • Databricks

Agent harness we build

Context

Tenant, role, record, workflow state

Tools

Your existing APIs, typed and scoped

Memory

Task state, retrieval, run history

Approvals

Permission checks and human gates

Evaluated, observable, reversible

Your product

Agent action

9 renewal reviews prepared, pending approval.

Same login, same permissions, same audit trail your customers already trust.

IntentContextTool callPermission checkHuman approvalAction in product

Why AI-generated code alone is not enough

Why AI-generated code alone is not enough for brownfield SaaS

AI-assisted coding can accelerate implementation. But it does not automatically understand the boundaries of a mature SaaS product: tenancy, permissions, business rules, integrations, approval paths, security requirements and failure handling.

Generated code is written against the code it can see, not against the commitments the product has already made. In a mature SaaS product those commitments are everywhere: tenant isolation that must never leak, permission checks that live in three different places for historical reasons, integrations whose partners have their own rate limits and contracts, business rules encoded in data rather than in code, and approval paths that exist because a regulator or a customer asked for them. An assistant can produce a plausible implementation that violates any of these without failing a single test. That is why brownfield AI still needs product context, deliberate architecture work and a production-ready agent harness around whatever writes the code.

AI coding can accelerate the build. The harness is what makes it production-ready.

What the layers produce on screen

One product, many places intelligence belongs

The same inspection and compliance platform, toggled between how it works today and how it works once the four layers are in place — then six angles we ship into established vertical SaaS. Two placements do the work: Jarvis in the universal search bar, driving the product's own screens, and Guidely as a docked assistant beside them.

Illustrative vertical SaaS workflow, not client work

  • Power users only
  • Training required
  • Eight clicks to a decision

app.meridiancompliance.com / inspections / queue

DashboardInspectionsSitesFindingsReportsAdmin

Inspection queue

5 of 5 records

Bulk actions
SearchRegionStatusInspectorDue dateSeverityClient
RefSiteStatus
INS-8841Harbor Point TerminalEscalate
INS-8846Bridgeport Depot 4Escalate
INS-8852Lakeview ProcessingReview
INS-8860Cedar Falls PlantScheduled
INS-8867Rio Verde YardScheduled

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

This is not a product we sell you. It is what we build into the product you already own, inside your codebase, your permissions and your audit trail.

01

Product experiences

The command bar, the summary panel and the guided tour that appear on the AI-native side.

02

AI-ready architecture

Inspection records, findings and templates exposed as retrievable, permissioned context.

03

Agent harness

Natural language mapped onto real product actions: filter, select, draft, navigate.

04

Production governance

The role check and the human approval step before anything is written or sent.

Industries and use casesIllustrative examples

Jarvis in the search barLogistics and TMS platform

insights.freightsuite.app / revenue / retention

FreightSuiteOverviewRevenueLanesCarriersCustomersAdmin
RevenueRetentionBookingsChurnCohortsForecastExports
Ask FreightSuite⌘K

Why did retention drop in the Northeast last quarter?

Applied to this view

region = Northeastperiod = Q2

Filtered the view and highlighted Q2. Three enterprise accounts downgraded after the June pricing change and carry 62% of the variance.

joins: subscriptions, tickets, usage_events · respects the viewer's account scope

Net revenue retention

5 of 5 records

Ask this view
Searchregion = Northeastperiod = Q2

Net revenue retention by period

Northeast · Q2 · 3 accounts drive 62% of variance

AccountSegmentStatus
ACC-2043Fulton Freight GroupDowngraded
ACC-2088Northline LogisticsDowngraded
ACC-2110Copper Ridge HaulAt risk
ACC-2154Bay State CarriersStable
ACC-2190Trailhead TransportExpanding

Same charts, same permissions. The query drove the product.

A copilot that answers the question behind the dashboard

Customers stop exporting to spreadsheets. They ask in plain language and the product's own chart and table redraw in front of them, with the tables behind the answer named.

  • Query planning over your real schema
  • The product redraws, not a chatbot

About the reference build

Guidely is our own build of this pattern, used here to show the idea. In your product it ships under your name, in your design system, on your APIs and inside your permission model.

See the reference build

Technology credibility

Depth without being tied to one framework or one cloud

We are not loyal to one AI framework. We understand the application, cloud, data and agent layers together, then choose the architecture that fits your existing environment.

AWS

AWS Select Tier partner, progressing toward Advanced Tier, with the required case studies completed.

AG-UI

Contributor to the AG-UI ecosystem. The AWS Strands integration was initially built by a Datics engineer before the AWS Strands team refined and approved it.

LangGraph and agentic systems

Long-running implementation experience across state, tools, approvals, orchestration and production agent workflows.

Databricks

Hands-on AI and agentic workflow experience in governed environments, including jobs, MLflow, vector search, SQL, Genie and Unity Catalog functions.

We build, modernize and migrate production SaaS and AI workloads across AWS, Azure and Google Cloud.

Cloud and AI modernization detail

Which layer is blocking your AI roadmap?

Tell us about the product and we will tell you what has to change before an agent can operate inside it.

Talk to an AI product engineer