AI Engineering

AI Feature Integration

Add chat, recommendations, smart search, and computer vision features without building an ML team.

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Smart Assistants
Process Automation
Predictive Models

Production AI workflow

Senior-led

Delivery map

AI Feature Integration

Add chat, recommendations, smart search, and computer vision features without building an ML team.

Feasibility

Define the task, baseline, and success threshold before model spend starts.

Guardrails

Evaluation, human review, and fallback behavior stay inside the workflow from the beginning.

Rollout

Serving, monitoring, logging, and iteration are planned with the product integration.

Workflow scope

Use-case framing and data audit
Model selection and evaluation
Guardrails and fallback paths
Monitoring and handoff

Service overview

What teams should know about AI Integration

Add ChatGPT-like assistants, smart search, and OCR/NFC-based scanning to your app. We handle prototyping, integration, and production reliability. Add chat, recommendations, smart search, and computer vision features without building an ML team. We integrate AI features—chat...

What you get

Senior delivery ownership with a cleaner execution path.

FocusAI Integration

DeliverySenior-led execution

SystemAI plus human review

We integrate AI features—chat assistants, semantic search, recommendations, and smart scanning—into existing apps or new MVPs.

We design AI as a context-aware service: hosted LLMs, vector stores, and prompt patterns. Engineers handle prompt design...

Included in the engagement

What is part of the engagement.

Best fit

Where this delivery model works best.

Pricing and delivery

Clear budget, delivery scope, and next steps.

We scope the work around delivery responsibility, execution risk, and what has to stay maintainable after launch.

01Estimate

$10K – $30K

Typical investment (USD)

Pricing reflects senior-led delivery ownership, structured scope, and implementation-ready outputs — not hourly output.

Per-feature PoV often starts lower; production integrations depend on data and scale.

02Timeline

3–6 weeks (feature dependent)

Typical delivery window

The exact window depends on scope depth, integration complexity, and the level of handoff or hardening required before launch.

Fast integration cycle.
Week 1: Requirements

Next step

Use a strategy call to turn this into a realistic scope.

We can clarify the scope, delivery risks, and the next step before we put a firm proposal in front of you.

Request a CallNo obligation. Clear next-step recommendation.

AI Feature Integration

What teams should know about AI Integration

Senior delivery ownership with a cleaner execution path.

What is part of the engagement.

Where this delivery model works best.

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Clear budget, delivery scope, and next steps.

Use a strategy call to turn this into a realistic scope.