Slow response times causing user drop-off
Synchronous API calls that force users to stare at loading spinners for 8 seconds instead of streaming tokens instantly.
Building an AI product requires more than a standard web app. We integrate custom LLM pipelines, vector databases, and real-time streaming interfaces into a functional MVP launched in 15 days.
If your product requires broader organizational AI diagnostics, enterprise LLM governance, or internal workflow automation, explore our dedicated AI consulting cluster.
AI products face unique pitfalls: slow model response latency, high token costs, and user frustration with hallucinations. Validating an AI concept requires production-grade engineering from Day 1.
Synchronous API calls that force users to stare at loading spinners for 8 seconds instead of streaming tokens instantly.
Unconstrained LLM responses that fail JSON parsing and break downstream UI components in production.
Spending 4 months setting up self-hosted fine-tuned models before validating whether real customers even want the feature.
We pair our confirmed 15-day MVP sprint with our production AI engineering stack.
Tech Stack for this Track
| Dimension | Traditional Alternative | With The Squirrel 15-Day MVP |
|---|---|---|
| Core functionality | Standard CRUD database forms with no automated intelligence. | Context-aware AI workflows that automate real cognitive tasks for users. |
| User feedback loop | Users manually entering and organizing all data. | AI extracts, processes, and presents insights with human feedback thumbs up/down. |
| Technical moat | Easily copied UI patterns with no proprietary prompt or retrieval setup. | Custom vector embeddings and refined system instructions tailored to domain data. |
From model selection to production deployment in two weeks.
Days 1–2
We define the exact AI task, evaluate sample inputs, and establish deterministic boundary rules.
Days 3–7
We configure the FastAPI backend, vector search, and build the Next.js streaming interface.
Days 8–12
We stress-test complex queries, handle API fallbacks, and integrate Stripe billing.
Days 13–15
We deploy to production, enable PostHog analytics and token logging, and hand over the code pack.
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
Our AI Consulting practice focuses on enterprise discovery, organizational readiness, and complex system roadmaps. The AI MVP Development service is an intensive 15-day execution sprint designed to launch a working software product to end users quickly.
Yes. We configure private pgvector or Pinecone embeddings with zero external model training, ensuring your company data remains strictly confidential.
We engineer deterministic fallback handlers and validation layers so that if a model response fails parsing, the system automatically retries or returns a graceful message rather than crashing the UI.
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