Brownfield AI
We help established vertical SaaS teams solve the product, architecture, agent and governance problems that keep meaningful AI from reaching customers. We start with the product you already run and change only what each capability requires.
AI Action Path
User intent
Product context
Allowed tools
Permission check
Approval
Action
Evaluation
Audit
User intent
Understand what the user is trying to accomplish, not just the words they typed.
Solutions
Each solution starts from a different blocker. The work underneath may touch one or several parts of the product.
Introduce multiple AI capabilities without turning the roadmap into a platform rewrite.
Competing work
One capability at a time
AI ideas and prototypes keep competing with an already committed product roadmap.
Prioritize the capabilities, build only the shared foundations they need, then ship them one at a time.
Expose the context, tools, data and permissions AI needs without reworking the whole platform.
Product core
AI-ready boundary
Core workflows, data, APIs and permissions were never exposed for agent use.
Expose only the tools, context, data and permission boundaries the capability needs, leaving the rest of the platform alone.
Give users visibility, interruption, correction and approval where it matters.
Agent, unsupervised
Supervised run
Users cannot see what the agent is doing, stop it mid-task or correct it before an irreversible action.
Add visible steps, interruption and correction paths, plus approval gates where the consequence warrants one.
Connect AI to the workflows, APIs and systems where work actually gets done.
Manual handoff
Connected action
The model produces a useful signal, but someone still has to notice it and complete the follow-up.
Connect the signal to context, permitted tools and approvals so the workflow can actually complete.
Design around the security, data and deployment boundaries your customers require.
Security review
Your environment
The capability works, then security or procurement blocks it on data, model access, residency, permissions or auditability.
Design tenancy, access, approvals, auditability and deployment boundaries into the capability from the start.
Turn a broad AI mandate into a prioritized first capability product and engineering can actually build.
Unresolved inputs
Decided direction
AI ideas, customer requests and prototypes exist, but nobody has a shared view of what should be built first.
Prioritize the capability portfolio, map dependencies and define the first capability to ship.
Engagement model
We start with a Planning Sprint to define the path, or an AI Scorecard if you have already built or tried AI ($5,000 and two weeks each; the fee counts toward production). Then we take the capability live at a fixed quote, and add more later only if you want it.
See how we workWhat changes inside the product
Each solution may touch one layer or several. We only change the parts needed to make it work safely in production.
AI becomes part of the existing product experience rather than a separate chat window. We bring intelligence into the screens, workflows and decisions customers already know.
What changes
Common blocker
The capability exists, but users cannot find it, trust it or use it inside the workflow where the work actually happens.
Support queue
In-product intent bar
The existing application needs to expose enough product context, tools, data and permission boundaries for AI to operate safely. We make only the targeted brownfield changes required by the capability.
What changes
Common blocker
The product was designed for humans and application code, not for an agent that needs to understand state and safely act.
Current product
AI-ready boundary
The model sits inside a larger operating layer that controls what the agent can know, call, change and be held accountable for. This is where agent behavior becomes dependable enough to work inside a live product.
Model and agent layer
Agent harness we build
Tenant, role, record, workflow state
Your existing APIs, typed and scoped
Task state, retrieval, run history
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.
A working capability only becomes a product capability when customers can approve, operate and support it. We design the controls and deployment boundary as part of the feature, not after the demo.
What changes
Common blocker
The capability works in a demo but stalls when security, procurement or enterprise architecture asks how data, actions and deployment are governed.
Security review
Your environment
Reference build
This illustrative vertical SaaS product shows what changes when AI gains the context, tools, permissions and controls to work inside the product instead of beside it.
Illustrative vertical SaaS workflow, not client work
app.meridiancompliance.com / inspections / queue
Inspection queue
5 of 5 records
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.
Illustrative use cases
The interaction changes with the product. These examples show AI working inside different workflows, data models, permissions and operating constraints.
Industries and use casesIllustrative examples
insights.freightsuite.app / revenue / retention
Why did retention drop in the Northeast last quarter?
Applied to this view
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
Net revenue retention by period
Northeast · Q2 · 3 accounts drive 62% of variance
Same charts, same permissions. The query drove the product.
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.
Guidely is Datics' reference implementation of this pattern. In your product, the capability ships under your name, in your design system, on your APIs and inside your permission model. See the reference build.
Proof
The screens above are illustrative. These were live products with real users, existing workflows and production constraints.
Dental & Veterinary · Practice Communications SaaS
AI communication and booking inside an established dental and veterinary practice platform.
Financial Services · Investment Operations Software
Production AI and automation added around a regulated .NET investment platform instead of rebuilding the core.
Healthcare · Clinical Intake SaaS
A working AI intake prototype evolved into a clinic-isolated, role-aware production SaaS.
Automotive & Dealerships · Customer Intelligence Platform
Fragmented dealership CRM, customer and inventory data unified into an actionable AI intelligence layer.
Technology depth
Mature SaaS rarely gives you the luxury of choosing a stack from scratch. We work with the application, agent, data and cloud layers already in place — and introduce new technology only where the capability requires it.
Agent & application systems
We work across the product experience and the agent runtime underneath it: state, tools, orchestration, approvals and the interfaces customers actually use, with hands-on work connecting agent runtimes to production product experiences.
Data & intelligence
Retrieval, vector search, SQL, model and evaluation workflows only matter when they can reach trustworthy product and business data with the right controls.
Cloud & deployment
We deploy into the environment the product already depends on, including customer-controlled cloud and more restricted deployment models when needed. AWS Select Tier Partner.
Production readiness
Production controls travel with every capability, regardless of model, framework or cloud.
Deployment flexibility
We fit the capability to the environment your product and customers already require.
Engineering ecosystem
Frameworks change. Production controls do not.
LangGraphCopilotKitDatabricksAWSMicrosoft AzureGoogle CloudWe build, modernize and migrate production SaaS and AI workloads across AWS, Azure and Google Cloud.
Cloud and AI modernization detailTell us about the product and we will tell you what has to change before an agent can operate inside it.
