AI Integration Services
We bring AI capability into the software your business already depends on, working over your own information, with the controls that keep it trustworthy. Our AI integration services put generative AI to work inside your systems through large language model APIs and the right guardrails, so your team can draft, summarize, search, classify, and reason across your private data without leaving the tools they know. We are an AI integration company that connects established models to your stack and builds the custom logic around them. We are not selling you a product to rent, and we are not training a foundation model from nothing. We make today’s best models useful on your data, safely.
This is the deeper end of AI work, for teams that have outgrown off-the-shelf bots and automations and want AI reasoning over their own documents, records, and processes. This page covers the scope we take on, how an integration project runs, what sets our approach apart, and how we price it. Our senior team has built software since 2018, every engagement is scoped to what you need and invoiced each month, the terms carry no long-term lock-in, and whatever we build belongs to you.
If you have a clear idea of what you want AI to do with your data, book a call and we will pressure-test it with you.
A fit example: law firms
This is most valuable for professional-services teams like law firms, where the knowledge lives in thousands of documents and reading them is the bottleneck. Consider a mid-size law firm that wants contract summaries drafted automatically from its own document store, so an attorney can ask for the key terms, dates, and obligations in an agreement and get a useful first draft in seconds instead of reading forty pages. We would integrate a language model against the firm’s private document repository, build retrieval that pulls only the relevant material, and create a tool inside the firm’s workflow that produces grounded summaries with citations back to the source, while keeping every document inside the firm’s own controlled environment.
The same approach fits accounting practices, consultancies, insurers, and any organization whose expertise is buried in documents and records that take too long to search and digest. Wherever skilled people spend hours reading to find or summarize information, an integration that reasons over your own data can hand them a head start.
Scope of work
AI integration is custom by nature, because it works against your specific data and slots into your specific software. Here is the range of work we take on.
We add generative capability into the applications your team already uses, drafting, summarizing, classifying, or answering, so the AI shows up where work happens instead of in a separate tool nobody opens.
We connect a model to your documents, records, or knowledge base and build the retrieval layer that feeds it only the relevant material, so its answers are grounded in your information and traceable back to the source rather than invented.
We build agents that carry out multi-step tasks on your behalf, gathering information, making decisions within rules you set, and taking actions across your tools, with a human in the loop wherever a decision carries real weight.
We integrate AI that reads, extracts, and structures information from contracts, forms, reports, and emails, turning piles of unstructured text into data your systems and your people can actually use.
We build assistants that let your team ask questions of your own policies, manuals, case files, or records in plain language and get accurate, sourced answers, so institutional knowledge stops living only in a few people’s heads.
We wire the language model into your databases, your document storage, and your other software through proper interfaces, with the access controls and logging that keep the integration accountable.
Around every integration we build the safety scaffolding: limits on what the AI can do, human review where it matters, logging of what happened, and fallbacks for when the model is unsure, because reasoning over real business data demands real controls.
AI features inside your existing software.
We add generative capability into the applications your team already uses, drafting, summarizing, classifying, or answering, so the AI shows up where work happens instead of in a separate tool nobody opens.
Retrieval over your own data.
We connect a model to your documents, records, or knowledge base and build the retrieval layer that feeds it only the relevant material, so its answers are grounded in your information and traceable back to the source rather than invented.
Custom AI agents.
We build agents that carry out multi-step tasks on your behalf, gathering information, making decisions within rules you set, and taking actions across your tools, with a human in the loop wherever a decision carries real weight.
Document understanding at scale.
We integrate AI that reads, extracts, and structures information from contracts, forms, reports, and emails, turning piles of unstructured text into data your systems and your people can actually use.
Internal knowledge assistants.
We build assistants that let your team ask questions of your own policies, manuals, case files, or records in plain language and get accurate, sourced answers, so institutional knowledge stops living only in a few people’s heads.
Connecting models to your stack.
We wire the language model into your databases, your document storage, and your other software through proper interfaces, with the access controls and logging that keep the integration accountable.
Guardrails, review, and oversight.
Around every integration we build the safety scaffolding: limits on what the AI can do, human review where it matters, logging of what happened, and fallbacks for when the model is unsure, because reasoning over real business data demands real controls.
How integration runs
A custom integration is a build, and you should never feel like the guinea pig for unproven work. We run it as a deliberate project with checkpoints you can see and approve. Here is the path from idea to a working integration.
A note on data handling and security, stated plainly because this work touches your most sensitive information. Your documents and records stay inside environments you own and control, and we build retrieval so the model is fed only the material a given task needs, with clear boundaries around what it can reach. Where data sensitivity calls for it, we use private or isolated model deployments, and your information is never used to improve a provider’s model unless you have given clear written permission. We log what the integration does, restrict access to what each part genuinely requires, and tell you candidly what a given model arrangement does and does not protect, without claiming certifications a provider does not actually hold.
Want a scope and a fixed price?
Where we stand apart
You could hand this to a large AI consultancy or attempt it with an in-house experiment. The reason custom AI projects so often stall is that the model was never the hard part, the data, the guardrails, and the integration are. Here is what we bring.
Grounded in your data, not guessing.
The difference between a useful integration and an embarrassing one is whether the AI answers from your real information or makes things up. We build the retrieval and the citations that keep it grounded, which is the work that decides whether anyone trusts the output.
Guardrails treated as part of the build.
We do not bolt safety on at the end. Limits, human review, logging, and fallbacks are designed in from the start, because AI reasoning over business data without controls is a liability, not a feature.
Senior engineers on the work.
The engineer who designs your integration is the engineer who builds it. We do not pass sensitive data to a junior bench to experiment on, which keeps the architecture sound and the risks understood.
You own the integration.
The code, the configurations, the prompts, the connections, and your data all remain yours. If we ever part ways, the working integration and its accounts stay in your hands, and we keep nothing.
No lock-in, ever.
The work is scoped, billed monthly, and runs month-to-month with no long-term contract. You continue because the integration earns its place, not because an agreement traps you.
Integration pricing
AI integration gets scoped around your goals, billed each month, and held to month-to-month terms with no long-term lock-in. There is no sticker price, because integrating a single AI feature against clean data and building a fleet of agents that reason across a sprawling, messy document store are entirely different undertakings, and pretending otherwise would be dishonest. After a discovery call and a review of your data and systems, we size the build to your circumstances and hand you a clear monthly number with nothing buried in it.
The language model usage and any supporting infrastructure carry their own running costs, billed to you by the providers and scaling with how much you use, and we estimate and explain those figures before you commit so the total picture is clear. A few honest factors shape the final number:
A single drafting or summarizing feature is far smaller than a multi-step agent that gathers information, decides, and acts across several systems.
Clean, well-structured, reachable data makes an integration faster and more accurate. Scattered or messy information needs preparation before any model can use it well.
Tighter privacy needs, private model deployments, and stricter controls add engineering work, and that care shows up in the scope.
A focused build differs from an engagement where we monitor accuracy, refine retrieval, and adapt the integration as your data and your needs evolve.
How complex the use case is.
A single drafting or summarizing feature is far smaller than a multi-step agent that gathers information, decides, and acts across several systems.
The state and accessibility of your data.
Clean, well-structured, reachable data makes an integration faster and more accurate. Scattered or messy information needs preparation before any model can use it well.
How sensitive your data is.
Tighter privacy needs, private model deployments, and stricter controls add engineering work, and that care shows up in the scope.
How much oversight and ongoing tuning you need.
A focused build differs from an engagement where we monitor accuracy, refine retrieval, and adapt the integration as your data and your needs evolve.
Ready to get a real number?
Questions before we start
Straight answers to what owners ask first.
What are AI integration services?
They are the work of building AI capability into the software and data you already have, so your team can draft, summarize, search, and reason over your own information without switching tools. We connect proven AI models to your systems and build the retrieval, the logic, and the guardrails around them, so the AI is grounded in your data and safe to rely on.
How is integration different from a chatbot or an automation?
A chatbot answers customers, and an automation moves data between apps. Integration is deeper: it puts AI reasoning over your private documents and records, inside your own software, often with custom agents. It is the right fit when you have outgrown off-the-shelf tools and need AI that understands your specific information.
Do you build your own AI models?
No. We integrate established large language model APIs and build the custom layer around them: the retrieval, the agents, the guardrails, and the connections to your systems. Training a foundation model from scratch would cost enormously and serve you worse than skillfully integrating a proven one, which is where projects actually succeed or fail.
How much do AI integration services cost?
The engagement is shaped around your goals, invoiced each month, and carries no long-term commitment, because every integration is custom to your data and systems. The model usage and supporting infrastructure carry their own running costs, billed by the providers, which we estimate up front. After a discovery call we give you a clear monthly figure.
How do you keep our sensitive data secure?
Your documents and records stay in environments you own, the model is fed only the material a task needs, and access is tightly restricted and logged. Where sensitivity demands it, we use private model deployments, and your data is never used to train a provider’s model without your written approval. We are candid about what each arrangement does and does not protect.
How do you stop the AI from making things up?
We ground it in your data through retrieval, so it answers from your real documents and cites the source rather than inventing. We add human review where output matters, set limits on what it can do, and build fallbacks for when it is unsure. We also test heavily for accuracy before launch and keep tuning after.
Can you build a custom AI agent for our specific process?
Yes. We build agents that handle multi-step tasks across your tools, gather information, make decisions within rules you define, and act, always with a human in the loop where a decision carries real weight. We design these to your process, not from a template, and wrap them in the same guardrails as every integration.
Will we be locked into a long contract?
No. The engagement is scoped and invoiced each month, with no long-term lock-in whatsoever. The integration, the code, and the accounts it runs in are yours, so whether to continue, pause, or change course is always your decision.
Scope your integration
If you know there is value locked in your own data and you want AI that can actually reason over it, the next step is a discovery call. We will pressure-test your use case, assess your data, and tell you honestly what an integration would involve, what it would protect, and the investment it would take.
Book a call to scope your integration, or read our case studies for a sense of how we work.