AI integration services, AI integration consulting, and enterprise AI solutions. We connect Claude, GPT-5, and open models to Salesforce, NetSuite, Zendesk, Slack, and the rest of your stack. From LLM integration to full AI system integration. First integration live in 30 days. Cost-of-ownership reported monthly, per integration.
AI integration is the practice of plugging LLMs and AI agents into the enterprise systems where work already happens: Salesforce, HubSpot, NetSuite, Zendesk, Intercom, Workday, Shopify Admin, Yardi, Guidewire. Unlike traditional API integration which moves structured data between systems, AI integration adds a reasoning layer that interprets free-text fields, drafts replies, classifies tickets, and decides which records to update. Unlike a standalone AI app, an integration writes back through native webhook handlers that are idempotent, rate-limit-aware, and reconcile on partial failure, so an LLM hallucination cannot corrupt the system of record. Common production patterns use Claude Sonnet 4.6 or GPT-5 with LangGraph for orchestration, tool-use schemas (OpenAI tools, Anthropic tool_use) to call the target API, and audit logs that map every write back to the LLM decision that produced it.
Every integration we ship looks like this loop. Data comes from your systems, AI does the judgment work in the middle, and the result writes back to the systems your team already uses. No new tabs to learn.
The AI integration solutions below are the patterns we ship most often. Whether you call it AI integration, LLM integration services, an AI integration platform play, or enterprise AI integration, the loop is the same. Almost every business has a candidate in at least three of these six categories; the audit step ranks them by ROI.
Salesforce ai integration is the most common entry point: connect Claude or GPT-5 to Salesforce, HubSpot, Pipedrive. Auto-enrich leads with account context, draft outreach, summarize call notes back to the opportunity record, push CRM updates without sales-rep workflow change. Same pattern fans out to HubSpot and Dynamics.
Integrate AI with NetSuite, SAP, QuickBooks, Xero. Invoice extraction, PO matching, anomaly detection, expense classification with audit trail.
Pipe AI into Zendesk, Intercom, Freshdesk, ServiceNow. Ticket triage, RAG over docs, draft replies, customer-sentiment scoring on every interaction.
LLM integration services across Slack, Microsoft Teams, internal portals. Meeting summarization, knowledge search across Notion, Google Drive, Confluence. The LLM integration services tier-1 is read-only retrieval; tier-2 adds tool-use into HRIS, project trackers, and code repos.
AI-powered document processing into S3, BigQuery, Snowflake. Extract structured data from contracts, claims, invoices, regulatory filings. See our intelligent document processing service →
Integrate Claude or Realtime API into Twilio, Aircall, Five9. Voice agents for tier-1 support, appointment scheduling, IVR replacement. See AI voice agent development →
We integrate against 50+ platforms today and build adapters for the rest. Tell us what's in your stack; the audit step will tell you what's worth integrating.
Tell us your stackFour phases, milestone-billed, with explicit kill points. We start with AI integration consulting (audit + design, the strategy work), then move to the integration build, then operate it in production. If the architecture doesn't fit your stack, you walk away at the design gate. No retainer trap.
One-week integration audit. We map your systems, data flows, auth model, and compliance constraints, then rank candidate workflows by effort and ROI.
One-week architecture phase. Pick the integration pattern (event-driven, API wrapping, RAG), choose models, define the eval suite. You sign off before any code ships.
Two-week build with mid-week demos. We integrate behind a feature flag, log every call, and run shadow-mode comparisons against baseline before production deploy.
Monthly cost-of-ownership reports. We watch model drift, retry rates, and integration failures. Most clients add the next integration in month two.
The list below is the most common. We integrate against modern SaaS APIs daily and build adapters for older systems where needed (SFTP, email, browser automation as a last resort).
Every platform comes with its own auth, write semantics, rate limits, and quirks. Pick a platform on the left to see how we actually wire AI into it. This is code we'd ship in week one of your pilot.
from getwidget.connectors import Salesforce
from getwidget.agents import EnrichmentAgent
sf = Salesforce.from_env() # JWT bearer auth
agent = EnrichmentAgent(model="claude-sonnet-4-6")
def enrich_lead(lead_id: str) -> dict:
lead = sf.get("Lead", lead_id)
summary = agent.run(
signals=[
web_search(lead["Company"]),
sf.history(lead["Email"]),
],
tone="executive-brief",
)
return sf.update("Lead", lead_id, {
"AI_Summary__c": summary.brief,
"AI_Score__c": summary.score,
"AI_Confidence__c": summary.confidence,
})
Not every team is ready. These are the three signals, across our shipped work, that consistently separate integrations that compound value from integrations that die at the demo.
Ticket triage, document classification, lead qualification, contract review. If your team spends 20+ hours/week on a workflow that requires reading and deciding, AI integration is likely the highest-ROI move this quarter.
Modern SaaS (Salesforce, HubSpot, NetSuite, Zendesk, Slack, Microsoft 365, Notion) all have APIs we can plug into without re-platforming. If data lives only in spreadsheets, that's pipeline work first.
Integrations that ship are the ones with a named sponsor and a measurable success metric: hours saved, deflection rate, revenue lifted. Without a sponsor, AI stays on the wishlist.
Most clients begin with the audit to scope, then run a 30-day pilot on the highest-ROI workflow, then move to monthly for the next three to five integrations.
Map your stack, score the integration opportunities worth doing.
One workflow shipped end-to-end against real systems in 5 to 8 weeks.
Embedded team for continuous delivery of the next integration on your roadmap.
The cases below are anonymized capability patterns drawn from real engagements. Named references shared under NDA once we know what you're building.
Sales reps spending 8+ hr/week researching accounts before outreach; lead records sparse and stale.
Claude-based enrichment agent pulls signals from web, LinkedIn, and CRM history. Writes structured summaries back to Salesforce lead records with confidence scores.
AP team manually keying PO data from supplier invoices; high error rate on multi-line invoices.
GPT-5 vision pipeline extracts PO data from supplier invoices, validates against ERP, routes exceptions to AP analyst with draft resolution attached.
Patient-facing helpdesk overwhelmed; tier-1 questions about coverage policy repeated daily across reps.
Retrieval-augmented support agent answers tier-1 patient questions from policy docs, drafts replies for agents, escalates compliance edge-cases automatically.
We turn down work where AI integration won't pay back. If any of these describe your situation, fix the underlying issue first, or use a tool that fits better. Honest signal beats a wasted pilot.
No system of record to integrate with. Fix the data layer first: a CRM, ERP, or warehouse with APIs. Until then, AI integration has nowhere to write back to.
Without a named sponsor with a budget and a measurable success metric, AI integration drifts into shelfware. We won't sign without the buyer, budget, and metric trio identified.
Deterministic pipelines don't need an LLM. If the workflow is rules-based with no natural-language reading or decisioning, buy Zapier or Make. An ai workflow integration platform is the right fit there, not a custom agency build.
There are valid reasons to pick each approach: in-house, consulting, middleware, or a focused agency. Seven dimensions, honestly:
Pricing and timelines reflect typical GetWidget engagements; alternative columns are generalizations from public pricing pages, RFP responses, and shipped client work.
A 30-minute fit call. We'll tell you honestly whether you need an integration agency, internal hires, or a middleware platform. No pitch.
AI integration is mostly a software-engineering discipline, not a model-selection exercise. These are the three anti-patterns that consistently kill projects, and how we work around them.
Team picks GPT-5, gets clean-notebook accuracy, then production data (overlapping CRM fields, missing emails, undocumented edges) collapses it. We build the data-quality probe + eval suite BEFORE touching the model.
Integration ships, team moves to next sprint, and the AI silently drifts. A CRM field changes, an API rate-limits, prompt-injection hits the chatbot. Two months later it's been broken for weeks. Fix: monitoring + runbooks + monthly review.
Multi-step agents with tool-use and reflection sound impressive in a demo. In production they fail unfixably. For 80% of integration work, one well-prompted LLM call + retrieval + deterministic plumbing beats a complex agent.
Book a free integration audit. We'll map your stack, name the highest-ROI workflows, and give you a 90-day roadmap with rough costs. No deck, no obligation to build.
Not sure if you need an integration partner, full AI development, or just consulting? These pages go deeper on each.
Production agents: ReAct, plan-and-execute, hierarchical multi-agent.
Production chatbots: web widget, WhatsApp, voice, Slack/Teams.
Production workflow automation in 6–8 weeks.
EHR integration on Epic / Cerner / athena / Veeva with BAA, PHI scrub, audit log.
Legal stack integration: Relativity / Everlaw / iManage / NetDocuments / Clio / Ironclad.
Pre-build audit + roadmap when the integration path isn't obvious yet.
SAP + OPC UA + Sparkplug B integration patterns for AI on the plant floor.
Connecting a custom AI knowledge base to Slack, Teams, Salesforce, Notion.
When the integration is one part of a full custom AI build — app shell, retrieval, agent layer, monitoring. The umbrella AI software development company pillar.
Token-cost projection for an integration's monthly LLM spend across Claude, GPT, and open-weights. Useful before scoping the integration budget.
How we catch regressions when an upstream API or schema changes — the methodology that keeps integrations from silently rotting.