Industry Solutions
AI Systems for
AI integration services that connect language models, agents, and AI features into the systems you already run — CRM, ERP, data warehouse, internal tools, and customer-facing surfaces. AI does not add value as a standalone chatbot. It adds value when it is wired into the actual workflow, on production infrastructure, with the data and permissions to do real work.
The Problem
Most AI Pilots Die Between the Demo and the Integration Layer
The hard part of an AI project is almost never the model. It is the plumbing — auth, schemas, eval, observability, permissioning — that lets the model actually touch your systems safely. That is where pilots stall and where good AI integration services earn their fees.
AI Lives in a Silo, Not in the Workflow
Your AI assistant works beautifully in a demo and is useless to your team because it cannot see the customer record, cannot file the ticket, cannot update the order, cannot read the latest contract. Without integration, AI is a clever search engine. With it, AI is a system that finishes work.
Integration Code Sprawls With No Owner
Every AI feature gets a different one-off integration written by a different team — different auth pattern, different rate-limit handling, different error surface. Six months in, nobody knows what is connected to what. The next AI feature pays the tax of fixing the last one's plumbing.
Data Is Stuck Behind Connectors That Cannot Hold
Off-the-shelf iPaaS connectors handle the easy 80% of an integration. The remaining 20% — custom auth, internal services, regulated data flows, anything that matters — sits behind a hand-written script that breaks every time an upstream API changes its schema. AI integration done right makes this layer first-class, not an afterthought.
How We Solve It
AI Wired Into Your Real Systems, Owned By You
Three integration patterns we ship most often. Each one is a real engineering project with proper auth, observability, evaluation, and rollback paths — not a Zapier script with an LLM step bolted on.
Direct, code-level integration between language models and your CRM, ERP, OMS, data warehouse, customer support tools, billing system, internal APIs, and on-prem services. Custom auth flows, permission boundaries, audit logs, and rollback paths. Tools designed and named for the way your business actually thinks, not the way the SaaS connector library happened to name them.
AI features embedded into your existing product or internal application surface — copilots, intelligent search, draft assistants, classification, summarization, decision support — that share auth, telemetry, and design language with the rest of the app. No bolt-on chat bubble; the AI is part of the product.
Vector stores, embedding pipelines, and retrieval-augmented generation systems that pull the right context from your data — across PDFs, databases, knowledge bases, internal wikis, and ticket archives — into the model's context window. Built with chunking and reranking strategy that survives production scale, not the toy version from the LangChain tutorial.
How we compare
Custom AI integration vs iPaaS connectors vs platform-owned AI
Three categories of AI integration that buyers compare during procurement. Each has a real use case — picking the wrong category is usually the expensive mistake.
| Dimension | Custom AI integration (us) | iPaaS / no-code (Zapier, Make, n8n + AI nodes) | Platform-owned AI (Salesforce Einstein, HubSpot Breeze, ServiceNow Now Assist) |
|---|---|---|---|
| Integration depth | Direct, code-level. Custom auth, custom mappings, internal services, regulated data flows — anything you can describe in writing, we can integrate. | Strong for common SaaS targets. Custom and on-prem integrations require workarounds or professional services hours. | Deep inside the platform; shallow outside it. Cross-platform AI workflows hit the platform boundary fast. |
| AI capability | Any model, any framework, any vector store. Swap models freely; mix frontier and open-weight per task. | Whatever the iPaaS vendor exposes — usually 1-2 LLM providers, basic tool use, limited memory/eval. | The platform vendor's AI features only. Roadmap is theirs, not yours. |
| Ownership | You own the code, the prompts, the eval set, and the model accounts from day one. | You own the workflow definitions. The runtime, integrations, and AI logic are vendor-owned. | Vendor owns everything. AI behavior changes when the platform vendor changes it. |
| Time to first production system | Weeks for a focused integration; 4-8 weeks end-to-end including auth, eval, and observability. | Days for a prototype. Weeks to harden it for production volume — that work usually surprises teams. | Days to enable the feature. Months to align your processes to what the platform AI actually does. |
| Cost shape over time | Build cost up front, predictable ongoing model + infra spend you can optimize. | Per-run or per-task pricing that grows with adoption. Plus the engineering hours to maintain the workflow graph. | Seat-based premium add-ons. The more useful the AI becomes, the faster the enterprise renewal grows. |
| Best fit | Production AI that touches your real systems and your real data — and that needs to keep working when the AI landscape shifts. | Lightweight internal automations with AI sprinkled in, prototypes, and personal/team productivity use cases. | Existing heavy customers of the platform extending into AI on top of an existing deep investment. |
4-8 Week Integration Pilot
Most AI integration projects ship faster than buyers expect once the architecture is right. A focused integration — language model plus one or two of your real systems with proper auth, evaluation, and observability — typically goes live in 4-8 weeks. Weekly demos on the real integration throughout. Additional integrations after the first cost less because the foundation is reusable.
Typical integration pilot
FAQ
Common Questions
AI integration services are engineering engagements that connect AI capabilities — language models, agents, embeddings, classification systems, generative features — into the systems an organization already runs. That means writing real integration code: auth flows, schema mappings, permission boundaries, evaluation harnesses, observability, and rollback paths. The AI capability itself is the small piece; the integration plumbing is the large piece, and that is where most pilots stall. Good AI integration services treat the integration layer as a first-class engineering surface rather than as a Zapier script with an LLM step bolted on. Specifically, AI integration covers connecting AI features to CRMs, ERPs, data warehouses, knowledge bases, internal APIs, customer-facing applications, and on-prem services — across both inbound (AI reads your data) and outbound (AI writes to your systems) flows.
Conceptually the same — connecting two systems so they can exchange data — but with three additional dimensions that classical integration does not have. First, the AI side is non-deterministic, so the integration layer needs to validate model output against a schema and route bad outputs to a retry or human review rather than letting them propagate into your system. Second, the AI side has its own cost and latency profile, so the integration layer needs to handle caching, rate-limiting, and fallback to cheaper models or canned responses when a frontier model is overloaded. Third, the integration has to expose its decisions for observability: every model call, every tool invocation, every fallback path needs to be logged and replayable so that when something goes wrong you can debug it. Classical integration handles none of that natively; AI integration treats all three as first-class problems.
For prototypes, internal automation, and lightweight team productivity workflows, yes — Zapier, Make, n8n, and the newer AI-native iPaaS players are good products and will let you ship in days. The wall is in three places. First, depth: iPaaS connectors handle common SaaS APIs well and custom or on-prem systems poorly; the latter is usually where the real business value lives. Second, runtime control: per-task or per-run pricing scales the wrong way as adoption grows, and the workflow graph becomes hard to debug at scale. Third, AI specifically: iPaaS platforms expose limited control over model choice, tool design, evaluation, and memory. For a one-off automation or an internal tool, those limits do not matter. For production AI that touches customers or moves money, they matter a lot, and that is where custom integration earns its fee.
A focused integration — one model, one or two of your real systems, real data, real auth, real observability — typically ships in 4-8 weeks from kickoff. The variation comes from your data readiness and how clean the upstream API contracts are; an old internal service with poorly-documented endpoints can add 2-3 weeks at the start. Once the first integration is live, the second one on the same foundation is usually 2-4 weeks because auth, observability, evaluation, and the AI middleware are already built. Most engagements start with a single high-leverage integration, harden it in production for 4-6 weeks, then expand to additional systems on top of the foundation. Weekly demos on the live integration throughout — not slide decks, not progress reports.
It depends on what your stack already speaks, not on what is trendy. For LLM-to-tool integration: Anthropic's Model Context Protocol (MCP), OpenAI function calling, LangGraph tool nodes, or pure-code tool wrappers depending on what fits the project. For backend integration: REST or GraphQL into your services, native database drivers, message queues (SQS, Pub/Sub, Kafka), webhooks, and where appropriate gRPC or direct SDK integration. For retrieval: Pinecone, Weaviate, pgvector, Postgres full-text when that is actually enough, or Elasticsearch when you already run it. For observability: LangSmith, Langfuse, Helicone, or OpenTelemetry into your existing logging stack. The architecture is loose enough that swapping any of these is a configuration change rather than a rewrite — so you are never locked into one vendor's roadmap.
Related services
See How We Can Help You
Let's talk about what's slowing down your ai integration services workflowand where AI can make the biggest impact. Free strategy call, no pitch deck.