AI Integration Services
Your product needs AI built into it. We handle the integration.
Choosing a model is the easy part. Making it work reliably inside your actual product — connected to your data, your workflows, your users — that’s the engineering problem. That’s what we solve.
Why most AI projects stall
Most companies don’t fail at AI because they picked the wrong model.
They fail because the model isn’t connected to the right data. Because the integration wasn’t designed for production. Because nobody thought about what happens when the output is wrong, the API is slow, or the cost spikes at scale.
We’ve integrated AI into SaaS platforms, mobile apps, and internal tools across real estate, healthcare, education, and enterprise software. Every one of those engagements required the same thing: treating AI integration as a product engineering problem, not a research exercise.
We bring that discipline to every integration we build.
Why AI integration is a systems problem
Calling an AI API is easy. That's not the work.
The work is designing how the AI connects to your existing systems. Which data it has access to. How it handles context across sessions. What it does when it produces a bad output. How you prevent costs from running away as users scale. How you keep the integration working when the underlying model gets updated.
These are systems problems. They require the same rigor as any other piece of production infrastructure.
That gap — between an AI prototype and an AI feature that works reliably at scale — is where most integration projects stall. We’ve spent years building real products in production. We know what that gap looks like and how to close it.
What we do
Four ways we build AI into your product.
AI Integration Strategy and Architecture
Before writing any code, we assess your existing systems, data, and workflows to understand where AI actually adds value. We design the integration architecture: which model fits the use case, how the AI connects to your data, what the integration patterns look like across your stack, and how to manage cost, latency, and reliability from the start. We also tell you what won’t work. Not every problem needs AI.
AI and LLM Integration Engineering
We build AI features into your product. This includes integrating large language model APIs (OpenAI, Anthropic Claude, Google Gemini, and others), building RAG pipelines that connect AI to your actual data, developing intelligent assistants and chatbots that live inside your product, implementing semantic search, and automating document-heavy workflows. The output is production-ready code, not a proof of concept.
Vertical AI Integration for Your Industry
We’ve integrated AI into products across real estate, healthcare, education, and enterprise SaaS. That domain knowledge matters. The compliance constraints, the data structures, the user expectations — these are different in each industry. We bring context, not just capability. Use cases we’ve worked on include AI for property search and document workflows, clinical intake and record processing, content generation for edtech platforms, and intelligent routing for enterprise operations.
Monitoring, Visibility and Ongoing Improvement
AI features need attention after launch. Models update, usage patterns shift, edge cases surface. We build in logging, output monitoring, cost tracking, and the visibility your team needs to understand what the AI is doing and how to improve it. We also stay involved post-launch to tune and iterate, because AI integration isn’t a one-time delivery.
How we work
A six-step path from systems assessment to production.
AI features behave differently at scale with real-world data. Our process is built to close the gap between a prototype and an integration that works reliably in production.
1. Assess your systems and data
We look at your current architecture, your data sources, and your workflows. We identify where AI integration delivers real value, what the data readiness looks like, and what risks or constraints need to be designed around upfront.
2. Design the integration architecture
We choose the right AI approach for your use case: which model, how to manage context and memory, how the AI connects to your data (direct access vs. retrieval pipelines), how to structure prompts for reliable outputs, and how to design for failure states. This design work happens before we write a line of code.
3. Build and integrate
We develop the integration, connect it to your existing systems and data, handle the edge cases, and build the API layers cleanly. This is engineering work: proper abstractions, error handling, security, and logging — not a clever script that breaks at scale.
4. Test against real inputs
AI features behave differently at scale with real-world data. We test with representative inputs, calibrate output quality, handle failure modes, and validate that the integration is consistent enough to ship.
5. Deploy and monitor
We deploy to production with the right monitoring in place: output quality, API costs, latency, and failure rates. We track these after launch and address what needs tuning.
6. Iterate and improve
AI integrations improve over time with real usage data. We stay involved post-launch, refine prompts and pipelines, and help you expand the integration as you identify new use cases.
The technology we work with
Production-grade tools across the integration stack.
AI and language model providers
OpenAI (GPT-4 and newer), Anthropic (Claude), Google (Gemini), and open-source models where the use case fits. We choose based on capability, cost, and data privacy requirements, not vendor preference.
Integration and infrastructure layers
REST APIs, microservices, event-driven architectures, middleware, and serverless functions. We build clean integration layers your team can maintain and extend.
Data and retrieval infrastructure
Vector databases (Pinecone, Weaviate, pgvector), document stores, SQL and NoSQL databases, and ETL pipelines. For RAG implementations, we design the retrieval layer alongside the AI layer so both are production-ready.
Application platforms
We integrate AI into products built on React, Angular, Node, Java, and mobile platforms (iOS, Android, React Native). If you have an existing codebase, we work within it.
Cloud platforms
AWS, Google Cloud, and Azure. Infrastructure, deployment, and monitoring aligned with your existing cloud environment.
Industries where we've integrated AI
Domain context, not just capability.
Real estate and proptech.
AI-powered search and matching for property platforms, document extraction and processing for transaction workflows, intelligent lead scoring, and natural language interfaces for investor and operator tools. We understand the data complexity and compliance sensitivity in this space and have shipped real products here.
Healthcare.
Intelligent intake and triage, AI-assisted document summarization for clinical workflows, patient and provider-facing assistants, and integrations with scheduling and records systems. We build with data privacy and regulatory requirements as baseline constraints, not afterthoughts.
Education platforms.
AI-driven content generation and feedback tools, adaptive learning features, administrative automation for student and teacher workflows, and intelligent search across course and resource libraries.
Enterprise SaaS.
AI copilots built into line-of-business applications, internal knowledge assistants that draw from company documentation, intelligent routing and classification for operations workflows, and AI-powered reporting and summarization.
Marketplace platforms.
Smart search and discovery, automated listing quality improvements, intelligent categorization, and AI-assisted matching for two-sided and multi-sided platforms.
Who we work with
Teams with a real product and a specific problem.
We work best with teams that have a real product and a specific problem they want AI to solve.
That means founders and CTOs at growth-stage startups who want to add AI features without rebuilding their entire product. Product leaders at mid-market companies who need AI integrated into an existing platform and want a team that understands both the engineering and the product context. Heads of engineering at software companies who need AI integration done properly and want to move faster than their current team can alone.
We don’t work well for: companies still trying to figure out whether they need AI at all. If that’s where you are, a strategy engagement makes more sense than an integration project. We can help you think through that too.
What makes this different
Most AI vendors lead with model capabilities. We lead with product engineering.
The difference matters when you’re trying to ship. Knowing which model is most capable doesn’t help you design the integration layer, handle the failure cases, keep token costs under control, or make the feature maintainable by your team six months from now. That work requires engineers who understand how to build production software, not just how to call an API.
We are a product engineering team. We’ve shipped software in industries where reliability is not optional. We bring that same discipline to AI integration.
We also won’t oversell what AI can do. If we think the approach you’re considering has real risks or limitations, we’ll say so before we build it.
What working with us looks like:
Engineers who understand your product, not just the AI layer
Integration designed for production from day one
Honest assessment of what will and won't work
Clean documentation your team can own and extend
Involvement after launch, not just delivery and exit
"We needed AI search built into our platform without rebuilding the whole product. GTC designed the integration cleanly, it shipped on time, and it actually improved how users found things. That was the measure that mattered."
"They were honest from the start about what AI would and wouldn't solve for our specific product. That scoped the project correctly from day one. The integration worked in production on the first try."
FAQ
Questions teams ask before they start.
AI development means building AI systems from scratch: training models, building ML pipelines, developing AI infrastructure. AI integration means taking existing AI capabilities (language model APIs, AI services, pre-built tools) and embedding them into a software product that already has a purpose. Most companies need integration, not development. You don’t need to train your own model to add intelligent search, an AI assistant, or document automation to your product. We focus on integration. If your use case genuinely requires custom model development, we’ll tell you that upfront.
Both. We integrate AI into existing products regularly. If you have a codebase, we come in, understand the architecture, and build the integration cleanly without breaking what’s already working. If you’re starting from scratch, we build the product with AI as part of the architecture from day one. The approach depends on your situation. We’ll be direct about which path makes more sense.
We look at your existing systems, data sources, and workflows in an initial scoping conversation. The key questions are: Is the relevant data accessible and structured enough to be useful? Are there existing API layers to build on, or does the integration require more foundational work? What are the compliance or security constraints? We identify gaps before we scope the work, so there are no surprises mid-project.
We work with all major providers: OpenAI, Anthropic Claude, Google Gemini, and open-source models where the use case fits. We choose based on what works best for your specific use case, your data privacy requirements, and your cost profile. We don’t have a preferred vendor. Some use cases are better served by a smaller, cheaper model. Others need the full capability of a frontier model. We’ll recommend what makes sense and explain why.
We design integrations so that sensitive data stays within the right boundaries. That means choosing appropriate API configurations, understanding what third-party providers log and retain, handling PII carefully, and building access controls into the integration layer. For healthcare and other regulated industries, we treat compliance requirements as starting constraints, not additions at the end. We’ll walk through the specific implications for your product before we build.
Cost management is part of the integration architecture, not an afterthought. We design with caching strategies, response length controls, tiered model selection (not every task needs the most expensive model), and rate limit handling. Before we build, we help you understand the expected cost profile at different usage scales so there are no surprises as you grow.
This is one of the most important design questions in any AI integration, and we address it before we start building. We design fallback states, output validation, confidence handling, and human review triggers where appropriate. The goal is to ensure a bad AI output doesn’t break the user experience or cause downstream harm. We build for failure from day one, not after it happens in production.
A focused integration for a specific feature — an AI assistant, intelligent search, or a document automation workflow — typically takes four to eight weeks from scoping to production deployment, depending on complexity and how ready your data and infrastructure are. Larger integrations across multiple product areas take longer. We scope clearly before we start so timelines aren’t moving targets.
We agree on success metrics before we build, not after. Depending on the use case, those might be: reduction in manual processing time, increase in user task completion rates, reduction in support volume, improvement in search relevance, or time saved per workflow. We build in the tracking to measure these from launch. AI integration should pay for itself. If we don’t think the use case has a clear ROI path, we’ll say so.
Yes. AI integrations need monitoring and iteration after launch. Models update, usage patterns shift, edge cases surface that weren’t visible in testing. We offer post-launch support and ongoing iteration to tune the integration, improve output quality, and expand AI use cases as your product grows. We design for long-term ownership by your team, but we stay available when you need us.
Ready to integrate AI into your product properly?
Ready to integrate AI into your product properly?
Tell us what you’re building and where you want AI to fit in. We’ll walk through what the integration would actually look like, what’s realistic, and where to start.
You’ll talk to a product engineer, not a sales rep.
No pitch. No proposal until we understand your product.