AI Integration Services That Fit Your Systems
Most businesses don't need another AI tool in another browser tab. They need AI working inside the CRM, ERP, help desk, data warehouse and apps their teams already use. Our AI integration services connect large language models, AI agents and automation to your existing systems, with the permissions, logging and human approvals that make them safe to rely on.
We handle the full AI implementation: choosing where AI pays for itself, preparing your data, building the integrations, testing against real scenarios and supporting the system after launch.
SERVICE OFFERINGS
Our AI Integration Services
Generative AI integration services
We add generative AI where it saves real time: drafting replies, summarizing calls and documents, extracting data from forms and answering questions from your own content. We integrate models from providers like OpenAI and Anthropic into your products and internal tools, with prompts, guardrails and evaluation built in.
AI implementation services
Many AI projects stall between the pilot and production. Our AI implementation services take a use case from proof of concept to a system your team uses every day, covering architecture, integration, testing, deployment, monitoring and training.
AI implementation consultant support
Not sure where AI fits? An AI implementation consultant from our team reviews your workflows, systems and data, then recommends the use cases worth building first, the right models and a realistic rollout plan.
AI data integration
AI is only as good as the data it can reach. AI data integration connects models and agents to your databases, data warehouses, document stores and SaaS tools, with access rules that match who is allowed to see what. Our data and analytics team prepares the pipelines and data quality underneath.
API integration and cloud integration
The integration of APIs is where AI turns into action: reading a record, creating a ticket, updating an order or booking a meeting. We build secure API and cloud integrations between AI services and your business systems, with authentication, rate limits, retries and a log of every call.
Where it fits, we use the Model Context Protocol (MCP) so AI agents can use your tools through one controlled interface.
SYSTEM ECOSYSTEMS
Where We Integrate AI
CRM integration and CRM automation
CRM integration connects your CRM to the other systems and channels your team uses, so customer data stays in one place instead of being copied between tools. Adding AI to that integration turns the CRM from a record-keeping system into one that does work:
- •Summarizing calls and emails and logging them to the right contact
- •Proposing field updates and next steps for a rep to approve
- •Scoring and routing new leads based on your own rules
- •Drafting personalized follow-ups for sales and marketing automation
- •Contacting new leads by voice and SMS and booking confirmed appointments
We integrate AI with CRMs such as HubSpot and with custom CRM systems.
ERP, data warehouse and reporting
Department teams often rebuild the same reports every week. AI agents connected to your ERP and data warehouse can pull the figures, prepare the analysis and flag exceptions, with role-based access that matches each department's data.
Help desk and customer service
AI integrated with your help desk can answer common questions from approved content, draft replies for agents, triage tickets and pass full context to a person when it escalates.
Documents and knowledge
Policies, contracts, manuals and support history become searchable in plain language, with every answer linked to its source passage.
Microsoft 365 and collaboration tools
We connect AI to email, calendars, documents and team chat so assistants can find information, schedule meetings and prepare drafts where your team already works.
Your own software and SaaS products
We integrate AI features into custom applications and SaaS platforms, including multi-tenant data isolation, usage tracking and safe rollout.
COGNITIVE AUTOMATION
Intelligent Process Automation and Customer Service Automation
Traditional automation follows fixed rules and breaks when the input changes. Intelligent process automation adds AI where a process needs judgment: reading an email, understanding a document or deciding which team should handle a request. The rest of the workflow stays as predictable, rule-based software.
PIPELINE ARCHITECTURE · UNSTRUCTURED TO ACTION
STRICT CONSTRAINTSMessy Input
Inbound email, PDF invoice, voice call recording, or support ticket.
Cognitive Extraction
LLM reads context, extracts structured fields, and resolves intent.
Schema Guardrails
Enforces type validation, business logic checks, and PII masking.
Human Checkpoint
Irreversible actions (money, delete, dispatch) wait for approval.
Deterministic Action
API call updates CRM, records calendar booking, or routes ticket.
Intelligent automation solutions we build
| Process | What AI handles | What stays rule-based |
|---|---|---|
| Lead follow-up | Conversations by voice and SMS, confirming a time | Sequences, calendar booking, CRM updates |
| Document intake | Reading forms, invoices and contracts, extracting fields | Validation, routing, approvals |
| Compliance checks | Comparing content against requirements | Sign-off by a specialist |
| Reporting | Pulling figures and writing the analysis | Schedules, permissions, distribution |
| Support triage | Understanding the request and drafting a reply | Ticket rules, SLAs, escalation |
Customer service automation
Customer service automation works best when AI handles the repetitive questions and people handle the rest. We build automation that answers from approved content, drafts replies for agents to approve, routes tickets by topic and urgency, and escalates with the full conversation attached. Conversational AI for customer service can run across chat, email and phone, connected to your help desk and CRM so every interaction is logged.
Built-in limits on what AI can do
Every automation we build has clear boundaries: which systems the AI can read, which actions it can take on its own, and which steps always need a person to approve. Anything irreversible, such as sending money or deleting records, stays behind a human checkpoint.
PROVEN DELIVERIES
AI Integration Projects We Have Delivered
Zonified Connect: AI integrated with CRM, calendars and phone
The challenge:
new leads waited for someone to call them back, and evenings and weekends went uncovered.
The integration:
voice and SMS agents connected to HubSpot, Google and Outlook calendars, Vapi and Twilio. They contact each new lead on the business's own sequence, log every conversation to the lead record, and book an appointment only after the lead confirms a time. Automation stops as soon as a person takes over.
Manufacturing Agent Platform: AI integrated with a data warehouse and Microsoft 365
The challenge:
a precision manufacturer with no in-house AI team needed department assistants its staff could trust and its own engineers could maintain.
The integration:
nine department agents, made up of ninety narrowly scoped subagents, connected to the company's enterprise data warehouse and Microsoft 365. Access is role-based and matched to each department's data. The client's own team maintains the platform after handover.
Insurance Claims RAG Assistant: AI integrated with policy and claims documents
The challenge:
claims professionals searched scattered files to answer coverage questions.
The integration:
an assistant over policy and claims documentation, built on PostgreSQL with pgvector, that answers in plain language and links each answer to the exact policy clause. Retrieval quality is monitored in production.
More projects
| Project | What we integrated |
|---|---|
| LabelRX | AI compliance checks of product labels against FDA and industry requirements |
| SparkUp AI | An AI workflow automation marketplace with a drag-and-drop builder |
| Agentic Engineering Platform | Agents that carry a requirement through planning, building, testing and review to a pull request |
DELIVERY METHODOLOGY
How Our AI Implementation Process Works
Every AI integration follows six stages. Each one ends with something you can review before the next begins.
Discover
We map the workflow with the people who do the work, list the systems involved and agree on what success looks like.
Architect
We choose the model, the integration points, the data access rules and the actions AI may take, and write down the trade-offs.
Build
We build the data connections, API integrations and AI logic, with a test set of real cases started on day one.
Validate
Your team checks the output against real scenarios, and we adjust until it fits daily work.
Deploy
We release with monitoring, logging, role-based access and documentation.
Evolve
We track quality in production, test model updates before switching and add new use cases.
Start small, then expand
The fastest route to value is one well-chosen workflow integrated properly, not AI everywhere at once. Once the first integration proves itself, the same data connections and controls make the next one faster.
ENTERPRISE SAFEGUARDS
Security, Data and Governance
Connecting AI to business systems means giving it access to real data and real actions. We design every integration so that access is limited, visible and reversible.
Permission-aware access
AI only reads data the current user is allowed to see, enforced in code rather than in a prompt.
Least-privilege actions
Each AI tool or agent gets only the actions it needs, with limits on volume and spend.
Human approval for high-impact steps
Anything irreversible waits for a person to confirm.
Full logging
Every AI request, answer and action is recorded for review and audit.
Data handling
We use model provider settings that exclude your data from training where they are offered, and can deploy inside your own cloud account when data must stay under your control.
Defense against prompt injection
Content from emails, documents and web pages is treated as data, never as instructions.
Monitoring after launch
We track accuracy, cost and failures, and test model updates before they reach users.
ENGINEERING EXCELLENCE
Why Choose Us for AI Integration
Software engineers first
10+ years of software delivery across web, mobile, enterprise platforms, data and AI. Integration is software work, and we treat it that way.
Production work you can read about
Published case studies covering CRM, data warehouse, document and phone integrations.
One team for AI, data and cloud
No hand-offs between an AI vendor, a data vendor and an integration vendor.
Narrow, reliable automations
We scope each AI task tightly so it does one job well before we expand it.
You own what we build
Code, prompts, test sets, infrastructure and accounts.
Top Rated Plus agency on Upwork
Top Rated Plus agency on Upwork, a status based on client feedback and contract history.
Two ways to work with us
Project engagement
A defined AI integration delivered end to end against clear milestones.
Dedicated team
AI and integration engineers who join your team, work in your repository and scale with your roadmap.
Technology we integrate with
| Layer | Technologies |
|---|---|
| AI models and frameworks | OpenAI and Anthropic models, LangChain, LangGraph, MCP |
| Business systems | HubSpot, Microsoft 365, Google Workspace calendars, custom CRM and ERP systems |
| Voice and messaging | Vapi, Twilio |
| Data | PostgreSQL with pgvector, MySQL, MongoDB, Redis, data warehouses |
| Cloud and DevOps | AWS, Azure, Google Cloud, Docker, Kubernetes, Terraform |
| Languages | Python, TypeScript, Node.js, ASP.NET |
FAQ
Common Questions
Everything engineering teams and business leaders need to know about connecting AI models, CRM automation, and data governance.
AI integration services connect AI models, agents and automation to the software a business already uses, such as its CRM, ERP, help desk, databases and apps. The goal is for AI to work inside existing workflows instead of in a separate tool.
CRM integration connects a CRM with other systems, such as email, calendars, phone, marketing tools and ERP, so customer data stays in one place and updates automatically. AI-powered CRM integration adds summaries, suggested updates, lead routing and drafted follow-ups.
AI development builds a new AI product or model. AI integration connects AI capabilities to the systems you already have. Many projects need both. For new AI products, see our AI development services.
No. AI integration works with your existing CRM, ERP and tools through their APIs. We only recommend replacing a system when it has no reliable way to connect.
Cost depends on the number of systems involved, the condition of your data, how many actions AI can take on its own, security requirements and ongoing model usage. A short discovery phase defines the scope before a fixed quote. Our AI development cost guide explains the main cost drivers.
A focused integration with one or two systems can reach a working pilot in weeks. Larger projects with several systems, approvals and compliance needs take longer. We deliver in stages so you see results early.
Intelligent process automation combines AI with rule-based automation. AI handles the steps that need understanding, such as reading documents or emails, while rules handle routing, approvals and system updates.
We design for it. AI only reaches data each user is allowed to see, high-impact actions need human approval, every action is logged, and we use provider settings that exclude your data from training where available.
Yes. We add generative AI features to SaaS products with tenant data isolation, usage tracking and gradual rollout.
Yes. We review your workflows and systems, recommend the use cases worth building first and plan a realistic rollout before any build starts.
Yes. We monitor accuracy, cost and failures, update integrations when connected systems change, and test new model versions before switching.
Start Your AI Integration Project
Bring us one workflow where your team loses time, plus the systems it touches. We will tell you where AI fits, what we would integrate first, what we would leave alone, and what it will take to run in production.