AISDContact

    AI Integration Services

    Add AI to what already works without rebuilding.

    AISD integrates AI into your existing products and workflows. Not a rip-and-replace. Not a 6-month R&D project. A production integration that ships in 4-8 weeks and generates ROI from day one.

    $25K+ fixed-price integrations · 4-8 wk to production · Eval harness included

    Integration patterns

    Four patterns. Pick the one that fits.

    01

    API-first AI embedding

    Connect frontier models (GPT-5, Claude, Gemini) into your existing product via clean API layers. Structured prompts, retry logic, fallback chains, and cost controls from day one.

    Use case: SaaS products adding AI features, internal tools augmenting with intelligence

    02

    RAG pipelines

    Retrieval-augmented generation over your proprietary data. Document ingestion, vector indexing, hybrid search, and grounded generation with citation tracking.

    Use case: Knowledge bases, customer support, legal document search, internal wikis

    03

    Model orchestration

    Multi-model routing where different tasks hit different models based on cost, latency, and accuracy requirements. A/B testing between models with eval-driven promotion.

    Use case: High-volume pipelines, mixed workload products, cost-sensitive deployments

    04

    Workflow automation with AI steps

    n8n, Zapier, or custom workflows with AI decision nodes: classification, extraction, summarization, and routing integrated into your existing business processes.

    Use case: Operations automation, document processing, lead qualification, support triage

    Tech stack

    We integrate with the tools that matter.

    OpenAI GPT-5 / GPT-5-miniAnthropic Claude 4Google Gemini 2.5LangChain / LangGraphPinecone / Weaviate / Qdrantn8n / Zapier / MakePydantic AIAWS / GCP / Azure

    What you get

    Every integration ships with these.

    • Eval harness. Automated test suite that measures accuracy, latency, and cost before and after deployment. Runs in CI.
    • Observability. Structured logging, cost tracking per model call, drift detection, and alerting. You see what the AI is doing.
    • Security review. Prompt injection testing, PII detection, rate limiting, and audit logging. Baked in, not bolted on.
    • Handoff runbook. Documentation, architecture diagrams, on-call procedures, and a 30-day post-launch support window.

    Integrations by system

    The systems we integrate AI into most.

    Each system has predictable AI integration patterns. Quick reference for what AISD ships into the systems you probably already run.

    Salesforce

    Lead enrichment + scoring agents. Account-level summarization surfaced inside the record view. Outbound email drafting with template-aware personalization. Deal-risk classifiers on pipeline data. Wired via REST API + Apex callouts; Einstein Trust Layer respected when present.

    HubSpot

    Marketing-email content generation tied to contact properties. Sequence-step drafting for sales reps. Inbound chatbot on HubSpot's chat widget routing to live reps when needed. Ticket summarization for service hub. Wired via HubSpot API + serverless functions; Operations Hub workflow integration.

    Snowflake

    Natural-language query interface (text-to-SQL) over governed Snowflake schemas. Exec dashboards augmented with AI-generated explanations of trend changes. Embeddings pipelines using Snowflake Cortex or external (Anthropic/OpenAI) + a vector layer. Row-level security preserved through the AI layer.

    Databricks

    ML workflows on top of Delta Lake. Feature engineering for classical ML models alongside LLM workloads. MLflow for model serving + observability. Delta Live Tables feeding RAG corpora. Strong fit for customers already on Databricks who want to add LLM/agent layer without leaving the platform.

    Postgres + pgvector

    Default RAG infrastructure for under 10M documents. Hybrid keyword + semantic search on a single index. No new vendor if you already run Postgres. Integrates with existing auth + RLS for permission-aware retrieval. Recommended for most mid-market workloads.

    Slack / MS Teams

    AI agents accessible inside Slack channels or Teams via slash commands. Internal knowledge agent on top of company-wide Slack history. Action-taking agents that file Linear/Jira tickets, post to GitHub, draft responses. Permission-aware so agents see only what the requesting user could see.

    Notion / Confluence

    RAG over internal docs. Search-and-summarize agents surfacing answers with citations. Drafting assistants that pre-fill page templates. Sync layer keeps the AI corpus fresh as docs change. Especially useful for engineering teams' onboarding + on-call runbooks.

    Zendesk / Intercom

    Customer-support deflection agents reading tickets + order data, drafting replies, escalating cleanly. AI-assisted agent workspace with reply suggestions. Ticket classification + routing. Integrates with the existing agent UI; no replacement of the help-desk.

    SAP / Oracle / NetSuite (ERP)

    Document processing agents for AP/AR (invoice extraction, 3-way match, exception routing). Procurement assistants drafting POs from natural-language requests. Audit-trail preservation for compliance. Read-only AI integration first, write paths through the existing service layer with full validation.

    Don't see your system listed? We've integrated AI into homegrown ERPs, internal Postgres apps, legacy Java systems, proprietary CRMs, and most cloud-native API surfaces. Bring the spec; we'll scope it.

    Next step

    Scope your AI integration in 30 minutes.

    Tell us what you're building and what systems need AI. We'll tell you which pattern fits and what it costs.