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Automate Operations

Operations AutomationPopular
Manual operations become production systems
Customer Operations
Onboarding & support that run themselves
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Lead routing & CRM hygiene, automated

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CRM/ERP IntegrationPopular
One source of truth across your tools
Internal Operations Platform
One operating surface for scattered tools

Build with AI

AI-Powered MVPPopular
Validated idea to production AI product
AI Feature Acceleration
Ship a real AI feature in your product
Prototype to Production
Turn a prototype into a real product

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Platform Modernization
Modernize a legacy platform safely
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1

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Automation
Workflows that run without chasing
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Operational Review
Map Your System
AI feature acceleration

Add useful AI features without creating unbounded product risk.

Define the decision, evaluate the behavior, and ship AI that has a clear fallback when confidence is not enough.

Book an AI feature reviewSee what you get
  • 20–30 minute review
  • No preparation needed
  • Clear decision boundary
AI Feature RuntimeIn production
  1. EXISTING PRODUCTLive app · real users
  2. AI FEATUREModel + prompt↩ back
  3. GUARDRAILSBounded · validated
  4. EVALUATIONScored vs. expected
  5. IN-PRODUCT RESPONSEBack to the UI
  6. TELEMETRYInstrumented · iteratedloop ↩
Existing product → shipped AILoop: telemetry → feature
Where AI features stall

The gap between a demo and a shipped feature

The prototype was the easy part. Here is where AI features get stuck on the way to production.

Stuck at prototype

The AI feature works in a notebook or a spike, but it has never survived contact with real users in the product.

Unclear how to productionize

No clear path from a promising demo to something the app can ship, monitor, and stand behind.

Model, UX, and guardrails

Getting the model, the interaction, and the safety boundaries right at the same time is where teams stall.

Integrating AI into an existing app

Wiring the AI into an established codebase, data model, and UX without destabilizing what already works.

What you get

A feature that ships and holds up

You end up with a production AI feature your users can rely on and your team can maintain.

A production AI feature

A real AI capability shipped inside your existing product — not a demo, a sandbox, or a slide.

Bounded, guardrailed behavior

Defined inputs, validated outputs, and safety boundaries so the feature behaves predictably in front of users.

Instrumented and iterated

Evaluation and telemetry built in, so you can measure quality, catch regressions, and improve deliberately.

How it works

The capabilities that deliver it

AI Feature Acceleration is built from three engineering capabilities working as one team.

Primary

Product Engineering

Engineering the AI feature into your existing product with the same rigor as the rest of the app.

Explore capability
Supporting

AI Systems

The models, prompts, agents, and decision logic that make the feature intelligent and bounded.

Explore capability
Supporting

Integration

Connecting the AI feature to the data, services, and workflows your product already runs on.

Explore capability

From prototype to production feature

Map the product problem

We define the real job the AI feature does for your users and where it fits inside the existing product.

1

Design behavior and guardrails

We shape the model, prompts, UX, and safety boundaries together so the feature behaves predictably.

2

Engineer into the app

We build the feature into your existing codebase and data model with production infrastructure and evaluation.

3

Instrument and iterate

We ship with telemetry and evaluation, watch real usage, and tighten quality feature by feature.

4
Is this the right starting point?

Where AI Feature Acceleration fits

A bounded AI feature added to a live product — with a decision boundary, an evaluation, and a human fallback.

Good fit when

  • You have a live product with real usage to add bounded AI into
  • There is a bounded decision the AI can improve, with a clear boundary
  • You have data to evaluate the AI behavior against
  • You want a defined human fallback when confidence is not enough
  • You intend to measure and iterate on feature quality

Not a fit when

  • There is no product yet to build the AI feature into
  • The AI has no measurable success criteria to evaluate against
  • You want AI for its own sake with no decision to improve
  • There is no boundary defined for what the AI is allowed to do
  • There is no plan to instrument, evaluate, or maintain it

AI Feature Acceleration questions

01

How is this different from just calling an LLM API in our app?

A raw API call is the easy 20%. AI Feature Acceleration is the other 80% — defining the product problem, shaping the UX, bounding the behavior with guardrails, evaluating quality, instrumenting telemetry, and engineering it into your existing app so it survives real usage. We ship a production feature, not a prototype behind a button.

Get your AI feature into production

Bring the feature you are trying to ship. We will map the product problem and show you what it takes to get it live, bounded, and instrumented.

20–30 minutes · No preparation needed