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AI Enablement & Integration

AI Integration Services
Built Into Your Product. Not Bolted On.

Build Me App provides AI integration services and AI enablement services for startups and established businesses. We embed AI agents, LLM-powered workflows, and assistants directly into your app, so your product does more with less.

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AI Integration and AI Enablement Services Company

Build Me App is a Toronto-based AI integration services company and Clutch Top Developer. We embed AI natively into digital products for startups and established businesses across Canada and the United States, not as a bolt-on chatbot, but as a core feature that makes the product fundamentally more valuable. We build large language model integrations, AI agents, retrieval augmented generation (RAG) systems, semantic search, and AI workflow automation using OpenAI GPT-4o, Anthropic Claude, Google Gemini, and Meta Llama. We are model-agnostic: we recommend the right model for your use case, not the one generating the most hype. Most AI integrations deliver their first production-ready feature within 3 to 6 weeks from the discovery call.

Imran Salahuddin CEO, Build Me App Clutch Top Developer, 20+ products shipped, Toronto, Canada

Last updated: July 2026, reviewed quarterly

6
Core AI use cases we build every day
20+
Products shipped with AI integration
Weeks
From use case to production-grade AI feature
5★
Rated on Clutch, Google & GoodFirms
Models & Platforms

AI Models and Platforms We Integrate.

We are model-agnostic. We evaluate the options during discovery and recommend the combination that performs best for your specific data, latency, and budget.

Foundation Models
OpenAI, GPT-4o
General-purpose reasoning, code generation, and vision tasks.
Anthropic, Claude
Complex reasoning and long-context document processing (200K tokens).
Google, Gemini
Very large context window (1M+ tokens) and multi-modal capabilities.
Meta, Llama
Open-source and self-hosted, no data leaves your infrastructure.
Vector Databases & Retrieval
Pinecone
Managed vector database, best for production RAG at scale.
Weaviate
Open-source with multi-tenancy, well-suited for SaaS platforms.
pgvector
PostgreSQL extension, best when you're already on Postgres.
AI Frameworks & Tooling
LangChain
Orchestration for complex multi-step AI chains and agents.
LlamaIndex
RAG-optimised, excellent for document processing pipelines.
Vercel AI SDK
Streaming AI responses in web apps with minimal overhead.

As part of every AI enablement services engagement, we evaluate the options during discovery and recommend the model, vector database, and framework combination that performs best for your specific use case, not the one generating the most hype.

What we build

Six AI Use Cases We Build Every Day.

Every engagement starts with identifying the right use case. Here are the most common ones we build.

AI Notes & Summaries
Automatic meeting notes, clinical documentation, and call summaries, AI that captures and organises information using embeddings and semantic understanding so your users don't have to.
AI Agents & Assistants
Autonomous agents that take actions, make decisions, and complete multi-step tasks on behalf of your users, inside your product, a core part of our AI agent development work.
Semantic Search
Search that understands intent, not just keywords. Built on vector embeddings and retrieval augmented generation (RAG), typically backed by a vector database like Pinecone or Weaviate.
AI Chat & Copilots
Context-aware chat interfaces and copilots, the foundation of our AI copilot development services, that help users get more out of your product, trained on your data, not the internet.
Workflow Automation
AI workflow automation that triggers actions, routes tasks, and automates repetitive processes, reducing manual work across your entire product.
AI-Powered Analytics
Intelligent dashboards and insights, backed by ongoing model evaluation, that surface what matters automatically, without users having to dig for it.
Who it's for

For Product Teams Ready to Ship AI That Works.

Not "exploring AI." For teams with a clear use case and a product that needs it built properly.

Startups Shipping AI Features
You need an AI feature shipped in weeks, not quarters. We take your use case from scoped to production without the back-and-forth of working with a team that's learning on your dime.
Teams with a Failing AI Build
Your team tried to build it but the results were inconsistent or the quality wasn't there. We come in, assess what exists, and either fix it or rebuild it, properly.
Businesses Falling Behind Competitors
Your competitors are shipping AI features and you're behind. We help you identify the highest-ROI use case and get it live fast, measured by business metrics, not vibe.
Existing Products

Add AI to an Existing App No Rebuild Required.

Adding AI to an existing app does not require rebuilding it. Most of our AI integration engagements involve products we did not build. We review your existing architecture during discovery, identify the right integration points, and add large language model capabilities, semantic search, or AI workflow automation in a way that is native to your existing codebase, not bolted on as a separate service.

If your existing system also needs broader modernisation beyond adding AI, outdated frameworks, technical debt, or an architecture that can't support new features easily, this is the right moment to address that alongside the AI work. See our Legacy App Modernisation service.
Architecture Review
We assess what you have and where AI integration points make sense in your existing stack.
Use Case Definition
The same use-case-first approach we apply to new builds, identifying exactly where AI creates the most value.
Model & RAG Selection
Choosing the right large language model and retrieval strategy for your existing data and architecture.
Integration & Testing
Building the feature into your live codebase with proper model evaluation before shipping to production.
Domain-Specific Models

AI Fine-Tuning Services Models That Know Your Domain.

Fine-tuning is the process of training a foundation model on your proprietary data, clinical notes, legal documents, financial records, product catalogues, so it learns the patterns and terminology of your specific domain, rather than only what it learned from the public internet.

When Fine-Tuning Is the Right Choice
  • Your use case needs consistent output structure that general models produce inconsistently
  • You need faster inference at lower cost than a large frontier model provides
  • Your domain has specialised terminology that general models handle poorly
When RAG Is Better Than Fine-Tuning
  • Your knowledge base changes frequently and needs to stay current
  • The model needs to cite sources or return traceable answers
  • You don't have enough labelled examples for effective fine-tuning
How it works

Use Case First. Production Second.

We never start with a model or a technology. We start with the problem you're solving and work backwards to the best AI implementation.

01
Use Case & Scoping
We identify the specific AI use case that will move the needle. Use-case-first roadmap before a line of code is written. Deliverable: scoped proposal + fixed price.
02
Data & Architecture
We assess your data, define the integration architecture, and choose the right model and approach. No over-engineering, the simplest solution that works in production.
03
Build & Evaluate
Production-grade prompts, evals, and fine-tuning. We build with monitoring from day one, so quality is measurable, not assumed.
04
Integration & Handover
Woven into your existing product flow. Full documentation, team training, and handover. No lock-in, you own everything.
Pricing

Scoped to Your Stack.

Every ai integration services project is different, the number of platforms, data complexity, and real-time requirements all affect scope. Whether you need custom ai development for a new feature or ai integration for startups building their first AI-powered product, we'll give you a clear fixed-price proposal after the discovery call.

What's included in every engagement
  • AI strategy & use case definition
  • LLM integration (any major model)
  • AI agent design & development
  • Workflow automation
  • Fine-tuning & prompt engineering
  • Testing, monitoring & handover
Get a Free Quote See Our Work
Model Comparison

AI Models We Integrate Compared.

A quick reference for the most common foundation models we work with. We recommend the right one for your use case during discovery.

Model Best For Context Window Hosting
OpenAI GPT-4o General reasoning, code generation, vision 128K tokens Cloud API
Anthropic Claude Long documents, complex reasoning chains 200K tokens Cloud API
Google Gemini Very large documents, multi-modal tasks 1M+ tokens Cloud API
Meta Llama Data must not leave your infrastructure 128K tokens Self-hosted
By the Numbers

AI Integration for Startups By the Numbers.

88%
of organisations now use AI in at least one business function, up from 55% in 2023. (McKinsey, 2026)
30%
of organisations have successfully scaled AI pilots to production. The gap between pilot and production is where most companies lose. (McKinsey, 2026)
$126B
forecast market size for AI integration services globally by 2028. (Grand View Research, 2025)
15–40%
accuracy improvement from fine-tuned models over general-purpose models on domain-specific benchmark tasks. (OpenAI, 2025)
800M
weekly active users on ChatGPT as of 2026, AI is no longer experimental, it's the expectation. (OpenAI, 2026)
Client Work

AI Integration in Action.

A sample of the AI-powered products we've shipped for real clients.

AI smart home maintenance platform
AIPropTechMobile

Smart Home Platform, AI-based maintenance assistant with semantic search & autonomous agents

An AI agent that helps homeowners stay ahead of maintenance, combining semantic search over maintenance history with proactive issue flagging powered by a large language model.

View case study
Diaspora student living platform
AIEdTechMarketplace

Diaspora Student Living Platform, Semantic search & embeddings-based recommendation engine

A recommendation system for international students using embeddings and semantic search to match students with services based on natural-language queries, not rigid category filters.

View case study
AI team knowledge management SaaS
B2B SaaSAI

AI Team Knowledge SaaS, RAG-powered organisational intelligence platform

AI-powered knowledge management SaaS that turns scattered team insights into a searchable, organisation-specific intelligence layer using RAG and LLM integration. SOC 2 Type 2 certified.

View case study
See All Case Studies

Build AI in. Ship Faster. Stay Ahead.

As a Toronto AI development company with hands-on experience across GPT-4o, Claude, and Gemini, we'll book a discovery call and map out exactly what's possible, and what's worth building.

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FAQs

AI Integration Questions, Answered Honestly.

No hype. Straight answers from a team that's shipped AI into production products across HealthTech, Fintech, SaaS, and more.

It depends on your use case. GPT-4o excels at general reasoning, code generation, and multi-modal tasks. Claude is best for long-context document processing and complex reasoning chains. Gemini offers a very large context window for large document collections. Llama is the right choice when data cannot leave your infrastructure. We evaluate the options during discovery and recommend based on your specific latency, cost, accuracy, and compliance requirements.
Most AI integrations deliver their first production-ready feature within 3 to 6 weeks from the discovery call. Complex multi-agent systems or fine-tuning engagements involving large proprietary datasets typically take 8 to 14 weeks.
Yes. Most of our AI integration engagements involve products we did not build. We review your existing architecture during discovery, identify the right integration points, and build the AI feature in a way that is native to your existing codebase, not bolted on as a separate service.
Retrieval augmented generation (RAG) is a technique that gives an AI model access to your specific documents before generating a response, rather than relying only on what the model was trained on. You need RAG when your AI feature has to answer questions about your specific content, support docs, product catalogue, internal knowledge base, rather than general knowledge.
Yes. For HealthTech and Fintech clients, we design AI pipelines with data residency, PII redaction, and audit logging built in from day one, not retrofitted after launch. We recommend engaging a compliance specialist for your specific regulatory context; we implement the technical architecture your legal team specifies.
Fine-tuning cost depends on the base model, dataset size, and how much iteration the evaluation process requires. It's scoped as part of the fixed-price proposal after discovery, we don't quote fine-tuning work before seeing your data and use case.