AI Integration Services
Embed Intelligence Into Your Systems
You don't need to rebuild your tech stack to use AI. We integrate OpenAI, Claude, and Gemini into your existing CRM, ERP, knowledge base, and workflows — adding intelligence where it matters most, without disrupting what already works.
What We Integrate
Six categories of AI integration. Each connects to different systems and solves different problems. We'll help you identify the highest-ROI starting point.
CRM Intelligence
Add AI-powered lead scoring, deal prediction, and automated follow-up suggestions to your existing CRM (Salesforce, HubSpot, or custom). The AI analyzes deal history, communication patterns, and engagement signals to surface the leads most likely to close — and drafts the email that will move them forward.
Systems: Salesforce, HubSpot, Pipedrive, custom CRMs
Real example: A B2B SaaS company added AI lead scoring to their HubSpot instance. The model analyzes website behavior, email engagement, and company firmographics to assign priority scores. Sales reps focus on the top 20% — close rate improved 35% in the first quarter.
Document Processing
Extract structured data from invoices, contracts, receipts, resumes, medical records, and regulatory filings. We build classification, extraction, and validation pipelines that replace manual data entry. The AI handles layout variations, handwriting, and multi-language documents.
Systems: Any document source — email attachments, file uploads, scanned documents, cloud storage
Real example: An insurance company processed 2,000 claims per day manually. We built an AI extraction pipeline that identifies document type, extracts relevant fields, cross-references against policy data, and flags anomalies for review. Processing time dropped from 8 minutes to 45 seconds per claim.
Intelligent Reporting
Add natural language querying to your existing data warehouse or business intelligence tools. Users type questions in plain English ('What were our top 5 products by revenue last quarter in the APAC region?') and get charts, tables, and explanations — without writing SQL or building dashboards.
Systems: PostgreSQL, MySQL, BigQuery, Snowflake, Power BI, custom data warehouses
Real example: A logistics company had 50+ Metabase dashboards that nobody used because finding the right chart took longer than just asking the analyst. We added a natural language layer that converts questions to SQL, generates visualizations, and caches results. Dashboard usage went from 12 to 340 queries per day.
Workflow Automation
Inject AI decision-making into your existing approval flows, routing rules, and business processes. The AI reads the context (email content, form submission, ticket description), classifies the intent, extracts key data, and routes it to the right team with a recommended action — replacing 15-step manual triage processes.
Systems: Email (SMTP/IMAP), Slack, Jira, ServiceNow, custom workflow engines
Real example: A consulting firm's intake process required a partner to read every new inquiry email, classify the service line, assess urgency, and assign it to a team. We built an AI classifier that reads incoming emails, extracts client name and project type, assesses urgency from language cues, and routes to the correct team Slack channel with a draft response. Partner time on triage dropped from 2 hours/day to 10 minutes.
Knowledge Base & Search
Add semantic search to your existing documentation, wikis, and knowledge bases. Users find answers by describing what they need in natural language, not by guessing the right keywords. The AI understands synonyms, context, and intent — and returns ranked results with highlighted excerpts.
Systems: Notion, Confluence, SharePoint, Google Drive, custom wikis, help centers
Real example: A 500-person company had 4,000 Confluence pages. The built-in search was keyword-based and nearly useless. We added a semantic search layer with document embeddings, cross-referencing between pages, and AI-generated summaries. Time to find information dropped from 12 minutes to 40 seconds on average.
Product AI Features
Add AI-powered features to your existing product without rebuilding it. Content recommendations, personalization engines, text summarization, image analysis, anomaly detection, and predictive features — integrated through your existing API layer. We add the intelligence; your product keeps its architecture.
Systems: Any product with an API — web apps, mobile apps, internal tools
Real example: An e-learning platform wanted AI-generated quiz questions from their existing course content. We built an API endpoint that accepts course text, generates multiple-choice questions with distractor analysis, and rates difficulty level. Instructors use it to create quizzes in 5 minutes instead of 2 hours.
Integration Architecture
Every integration we build has six architectural layers. This isn't a direct API call from your app to OpenAI — that approach breaks at scale, leaks data, and costs 3x more than it should.
API Gateway Layer
All AI calls go through a centralized gateway that handles authentication, rate limiting, cost tracking, and model routing. This layer also manages API key rotation, request/response logging, and fallback logic when a provider has an outage.
Transformation Layer
Your data needs preprocessing before it reaches the LLM: PII redaction, context assembly, prompt formatting, and token optimization. After the LLM responds, we validate the output, extract structured data, and transform it into the format your systems expect.
Integration Adapters
Purpose-built connectors to your existing systems. We map your data model to the AI pipeline and back. Each adapter handles authentication, pagination, error handling, and data format conversion for its target system.
Caching & Cost Control
Semantic caching stores AI responses so identical (or semantically similar) queries are served from cache. Model routing sends simple requests to cheaper models. Token budgets enforce per-user and per-tenant limits. Together, these typically reduce AI costs by 50-70%.
Monitoring & Observability
Real-time dashboards showing request volume, latency, error rates, cost per integration, and model performance metrics. Alerting for anomalies — unusual cost spikes, increased error rates, or degraded response quality. Full audit trail for compliance.
Security & Compliance
Data encryption in transit and at rest. PII detection and redaction before data reaches external APIs. Role-based access to AI features. Audit logging for every AI interaction. Compliance documentation for SOC2, HIPAA, and GDPR requirements.
API Cost Analysis
AI API costs vary 100x between models. Choosing the right model for each task is the single biggest cost lever. Here's what each provider actually costs in production.
| Model | Input Cost | Output Cost | Per Request | Best For |
|---|---|---|---|---|
| GPT-4o | $2.50 / 1M tokens | $10.00 / 1M tokens | $0.01-$0.04 per request | Complex reasoning, multi-step tasks, code generation |
| GPT-4o-mini | $0.15 / 1M tokens | $0.60 / 1M tokens | $0.001-$0.003 per request | Classification, extraction, simple Q&A — 90% of integration tasks |
| Claude 3.5 Sonnet | $3.00 / 1M tokens | $15.00 / 1M tokens | $0.01-$0.05 per request | Long documents, nuanced analysis, creative writing |
| Claude Haiku | $0.25 / 1M tokens | $1.25 / 1M tokens | $0.001-$0.005 per request | Fast classification, routing, short-form extraction |
| Gemini 1.5 Flash | $0.075 / 1M tokens | $0.30 / 1M tokens | $0.0005-$0.002 per request | High-volume processing, multimodal (images + text) |
Key insight: GPT-4o-mini handles 90% of integration tasks (classification, extraction, routing) at 1/20th the cost of GPT-4o. We use expensive models only where quality demands it — and we measure the difference.
Security Checklist
Every AI integration ships with these 10 security measures. This isn't optional — it's the minimum for production AI.
Pricing
Fixed prices based on the number of systems, features, and security requirements. AI API costs are billed separately by the provider — we optimize them aggressively.
Single Integration
Add AI to one system or workflow.
Multi-System
AI across multiple systems with orchestration.
Enterprise
Full AI layer across your tech stack.
AI Integration Resources
Guides on API integration architecture, prompt injection defense, cost optimization, and building AI into existing products.
Explore the AI HubAI Integration Security Checklist (PDF)
The 10-point security checklist we use on every AI integration project. Covers PII handling, prompt injection defense, data residency, and compliance documentation.
Proven Results
Real projects. Real numbers. See what we delivered.
AI Support Agent: Resolving 73% of Tickets Without Human Intervention
73% ticket auto-resolution, 4hr → 8min response time
An AI customer support agent that handles Tier 1 tickets via chat and email, resolves 73% automatically, and escalates the rest with full context to human agents.
AI Voice Agent: Automated Appointment Booking via Phone
Missed calls reduced from 40% to 3%, 120 appointments/month booked by AI
An AI phone agent that handles inbound calls for a dental practice, books appointments, answers FAQs, and reduces missed calls from 40% to 3%.
AI Sales Agent: Automated Lead Qualification and Meeting Booking
Lead response time: 4 hours → 90 seconds, qualified meetings up 2.4x
An AI sales development rep that qualifies inbound leads via chat and email, scores them using BANT criteria, and books meetings directly on reps' calendars.
Frequently Asked Questions
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