Hallucinations damaging business credibility
Models generating plausible-sounding but completely fabricated numbers, policy quotes, or technical instructions.
Harness foundation models for mission-critical enterprise workflows. We architect retrieval-augmented generation (RAG) pipelines, multi-agent systems, and voice intelligence with guaranteed zero hallucinations and private cloud hosting.
Chatting with an LLM in a browser window is simple. Connecting foundation models to enterprise databases, internal permissions, CRM systems, and real-time voice lines requires specialized engineering.
Models generating plausible-sounding but completely fabricated numbers, policy quotes, or technical instructions.
Internal documentation buried in PDFs, Notion, Google Drive, and databases that employees spend hours searching through manually.
LLMs that can write text but cannot reliably trigger calendar bookings, database updates, or CRM lookups without breaking.
Risking customer data leakage by sending unstructured prompts to unvetted external AI vendors.
We design, build, and deploy production-grade GenAI pipelines using industry-standard enterprise frameworks.
Supported GenAI frameworks & models
We architect model-agnostic pipelines so you can switch between OpenAI, Anthropic, or open-source models as pricing and benchmarks evolve.
| Operational Dimension | Without Clear Architecture | With The Squirrel |
|---|---|---|
| Source accuracy | Answers based on general pre-training data; frequently hallucinates internal facts. | Answers strictly restricted to verified corporate sources with clickable citations. |
| System integrations | Isolated text box with no connection to internal software. | Bidirectional sync with Salesforce, PostgreSQL, HubSpot, and Slack. |
| Data privacy | Unclear data retention policies on public interfaces. | Zero-retention enterprise API keys and private vector storage. |
A battle-tested 4-phase roadmap from knowledge curation to live monitored rollout.
Phase 1
We clean and structure your target documents and select the ideal vector database and embedding model.
Phase 2
We connect LangChain or LlamaIndex workflows with your CRM, calendar, or internal APIs.
Phase 3
We stress-test the model with adversarial prompts, edge cases, and confidence thresholds.
Phase 4
We launch with conversation telemetry and latency monitoring on enterprise AWS infrastructure.
A two-year technical partnership delivering two parallel engines: a suite of internal web applications and dashboards for operational clarity, and an AI-powered data scraping and outreach system to fuel B2B sales.
Internal Tools Delivered
Lead Sourcing Automated
We use Retrieval-Augmented Generation (RAG) with strict negative constraints. If the model cannot find direct support in your approved source documents, it is instructed to report that it does not know and route the inquiry to a human.
Yes. Our pipelines are built with model-agnostic abstraction layers, allowing you to use Claude Sonnet 5, GPT-4o, or private open-source models based on accuracy and speed requirements.
Yes. Through function calling and tool execution, the AI can validate user input and safely trigger API endpoints to update records, schedule meetings, or create support tickets.
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