Provider-agnostic LLM orchestration layer on Vercel Edge Functions with intelligent routing between Claude, GPT-4o, and Gemini. RAG pipelines use Supabase pgvector for hybrid vector + relational search with cross-encoder re-ranking, backed by event-driven document processing on Inngest/Trigger.dev for durable serverless workflows. Next.js frontend with Vercel AI SDK handles streaming responses and role-based access control.
Where enterprise projects fail
Claude, GPT-4o, and Gemini all have different API contracts, different rate limit behaviors, and they fail in completely different ways. So you end up with engineers spending 6+ months -- sometimes longer -- building and maintaining provider abstraction layers just to keep the lights on. That's not shipping. That's treading water. And the real kicker? Every time one of these providers updates their API or changes their token limits, you're back in the weeds. We've watched promising AI products stall completely because the infrastructure complexity ate the roadmap whole. Teams in New York, Austin, London -- doesn't matter where -- they all hit the same wall eventually. The actual business logic, the features your users care about -- those keep getting pushed to next sprint. Then the sprint after that. It's a genuinely painful problem, and it compounds the longer you wait to address it properly. What starts as a two-week abstraction task quietly becomes a six-month engineering sinkhole, and by the time anyone calls it what it is, you've burned through runway that was supposed to fund actual product development. We've seen this kill momentum at companies that had everything else going for them -- solid funding, great domain expertise, real user demand. The infrastructure complexity just ate them alive before they could ship anything worth talking about.
But real enterprise documents are a disaster -- scanned PDFs from 2009, tables with merged cells, Word files where someone's been copy-pasting since Obama's first term. Accuracy falls apart fast. And in regulated industries like finance or healthcare, a hallucinated output isn't just embarrassing -- it's a compliance exposure that can cost you real money and real trust. We're talking potential SEC scrutiny or HIPAA headaches, not just an awkward conversation with a client.
There's no actual pipeline connecting ingestion to the workflows that need the output. That gap kills your ROI on AI spend. Honestly, it's like buying a Ferrari and leaving it in the garage because you haven't built the driveway yet. The model isn't the hard part -- the plumbing around it is.
Everything looks fine in staging, then you hit production scale across three LLM providers and suddenly nobody knows which team ran up a $40,000 bill in February. Without per-department visibility and actual enforcement, "unpredictable monthly API costs" is putting it charitably. Budgets get blown. Finance gets angry. Engineers get blamed. And then everyone spends two weeks in retrospectives instead of building anything.
What we deliver
Enterprise AI Integration Is an Architecture Problem, Not a Prompt Problem
Every engineering team can wire up an OpenAI API call. The hard part is building the system around it: managing multiple LLM providers, orchestrating retrieval-augmented generation pipelines against your actual document corpus, handling failover between Claude and GPT-4o, and doing all of it at enterprise scale with audit trails and access controls.
We build AI integration platforms as proper software systems — not demo-ware. Next.js frontends with real-time streaming responses, Supabase for vector storage and auth, serverless functions that orchestrate multi-step LLM workflows, and document processing pipelines that handle the messy reality of enterprise data.
Why In-House Teams Stall on AI Integration
Most engineering teams hit the same walls when moving from AI prototype to production:
The Multi-Model Orchestration Gap
You need Claude for nuanced analysis, GPT-4o for structured extraction, and Gemini for multimodal processing. Each has different API conventions, rate limits, token pricing, and failure modes. Building a unified orchestration layer that intelligently routes requests, handles fallback, and normalizes outputs across providers is a full-time architecture project. It's not glamorous work, but it's the work that actually matters.
RAG Pipeline Complexity
Retrieval-Augmented Generation sounds straightforward until you deal with real enterprise documents. PDFs with tables that break extraction. Legacy Word docs with inconsistent formatting. Regulatory documents where a missed clause has legal consequences. Chunking strategy, embedding model selection, vector store tuning, re-ranking — each is a rabbit hole that eats weeks of engineering time.
The "Last Mile" Integration Problem
An AI that generates great answers in a notebook means nothing if it can't plug into your existing workflows. CRM updates, document management systems, approval chains, Slack notifications, audit logging — the integration surface area is where most internal AI projects die. Not because the AI failed. Because the plumbing did.
Our Architecture: How We Build AI Platforms That Actually Ship
We've developed a battle-tested architecture pattern for enterprise AI integration that we adapt to each client's requirements.
LLM Orchestration Layer
We build a provider-agnostic orchestration service that sits between your application and the LLM providers. This handles:
- Model routing: Intelligent selection between Claude 3.5 Sonnet, GPT-4o, and Gemini based on task type, cost constraints, and latency requirements
- Failover and retry logic: When OpenAI has an outage (and they do), requests automatically route to Claude or Gemini with prompt adaptation
- Token budget management: Real-time tracking and enforcement of per-user, per-department, and per-project token budgets
- Response streaming: Server-sent events through Next.js API routes for real-time streaming responses in the UI
This layer runs on Vercel Edge Functions or AWS Lambda, depending on your infrastructure. We use LangChain for chain orchestration where it adds value, but we're not afraid to write custom orchestration when its abstractions get in the way.
RAG Pipeline Architecture
Our RAG pipelines are built for precision, not just recall.
Document Ingestion: A multi-stage pipeline — Apache Tika or Unstructured.io for raw extraction, custom parsers for domain-specific formats (legal docs, financial reports, technical manuals), and intelligent chunking that respects document structure rather than blindly splitting on token count.
Vector Storage: Supabase with pgvector for most deployments. You get vector similarity search alongside traditional relational queries, row-level security for multi-tenant document access, and real-time subscriptions for pipeline status updates. Clients needing dedicated vector infrastructure get Pinecone or Weaviate.
Retrieval and Re-ranking: We implement hybrid search combining dense vector retrieval with BM25 keyword matching. A cross-encoder re-ranking step dramatically improves precision on domain-specific queries. This approach consistently outperforms naive vector similarity, especially for technical and regulatory content.
Generation with Grounding: Every generated response includes source citations with page-level references back to original documents. We implement hallucination detection through a secondary verification pass that cross-references claims against retrieved chunks.
Document Processing Workflows
We build document processing as event-driven pipelines using serverless functions:
- Intake: Documents land via upload UI, email integration, or API webhook
- Classification: An LLM classifies document type and routes to the appropriate processing pipeline
- Extraction: Structured data extraction using function calling (GPT-4o) or tool use (Claude) with schema validation
- Enrichment: Cross-referencing extracted data against existing records in your systems
- Action: Triggering downstream workflows — CRM updates, approval requests, notifications, database writes
Each step is independently observable, retryable, and logged for compliance.
Frontend: The Interface Layer
We build AI interfaces in Next.js with the Vercel AI SDK for streaming. That means:
- Sub-second time-to-first-token for chat interfaces
- Real-time document processing status with progress indicators
- Markdown rendering with syntax highlighting for technical content
- Mobile-responsive interfaces that work for field teams, not just desktop users
- Role-based access control integrated with your existing auth provider
Technology Stack in Detail
Every tool in our stack is chosen for production reliability, not novelty:
- Next.js 14+ with App Router — server components for initial data loading, client components for interactive AI features
- Vercel AI SDK for streaming LLM responses with built-in provider abstractions
- Supabase for vector storage (pgvector), authentication, real-time subscriptions, and edge functions
- LangChain / LangGraph for complex multi-step agent workflows where stateful orchestration is required
- Anthropic Claude, OpenAI GPT-4o, Google Gemini APIs with our custom provider abstraction layer
- Pinecone or Weaviate for dedicated vector search at scale when pgvector reaches its limits
- Vercel for deployment with edge function support and automatic scaling
- Inngest or Trigger.dev for durable workflow execution — document processing jobs that survive function timeouts
Proven in Production
These architecture patterns weren't developed in a vacuum.
When we built a directory platform managing 137,000+ listings with complex search and filtering, we developed the data pipeline and indexing architecture that now powers our RAG ingestion systems. Shipping 91,000+ dynamically generated content pages with Lighthouse scores above 95 proved we could build performant frontends on top of heavy data processing layers.
Our real-time auction platform processing bids at sub-200ms latency taught us how to build low-latency streaming architectures — the same patterns we use for LLM response streaming. Deploying across 30 languages for a Korean manufacturer proved out our multi-tenant, internationalized architecture.
These aren't AI-specific wins. They're the production infrastructure experience that keeps our AI platforms from falling apart under real load.
Delivery Model and SLAs
AI integration projects follow our standard delivery framework with AI-specific additions:
- Discovery Sprint (2 weeks): We audit your document corpus, map your workflow requirements, benchmark LLM performance against your actual data, and deliver an architecture document with cost projections for LLM API usage at your expected scale
- MVP Build (6-8 weeks): Core RAG pipeline, primary LLM integration, document processing for your highest-value workflow, deployed to staging with monitoring
- Production Hardening (3-4 weeks): Failover testing, load testing with realistic document volumes, compliance review, audit logging verification, team training
- Ongoing Optimization: Monthly model performance reviews, prompt tuning based on production data, new model evaluation (we tested Claude 3.5 Sonnet the week it launched for active clients)
We guarantee sub-2-second response times for standard RAG queries and 99.9% uptime for the platform layer. LLM provider uptime is their SLA to own, but our failover architecture limits the blast radius. You get weekly progress updates and deployed preview environments throughout the build.
When This Makes Sense
This engagement fits if you've got substantial document volumes that need intelligent processing, existing workflows that'd benefit from LLM-powered automation, and an engineering team that should be focused on your core product — not becoming AI infrastructure specialists.
It's not the right fit if you need a simple chatbot widget (dozens of SaaS tools handle that fine) or if your primary need is model training rather than application integration.
See this capability in action
Frequently asked
How do you handle failover between multiple LLM providers like Claude, GPT-4o, and Gemini?
We build a provider-agnostic orchestration layer that's watching API health, latency, and error rates in real time. When a provider degrades or starts returning 529s, requests automatically reroute to the next-best available model -- with prompt adaptation to handle the differences in how Claude versus GPT-4o versus Gemini expects instructions to be formatted. Token budgets and cost constraints factor into those routing decisions too, not just raw performance. And honestly? No manual intervention required when OpenAI has a bad Tuesday morning. Your users don't notice. Your on-call engineer doesn't get paged at 2am. That alone is worth a lot.
What vector database do you recommend for enterprise RAG pipelines?
For most deployments, we start with Supabase and pgvector -- you get vector search running right alongside your relational queries, row-level security for multi-tenant access, and one fewer infrastructure dependency to explain to your DevOps team. But clients processing millions of documents or needing sub-10ms retrieval are a different conversation. Those get dedicated vector stores -- Pinecone or Weaviate -- running alongside the primary database. It's not a one-size-fits-all call. It depends on your actual query volume and latency requirements, not what sounds impressive in a pitch deck.
How do you reduce hallucinations in RAG-powered AI responses?
We use a multi-layer approach because no single technique gets you there alone. Hybrid retrieval combines dense vectors with BM25 keyword matching. Cross-encoder re-ranking improves chunk relevance before anything hits the LLM. System prompts include strict grounding instructions. Then a secondary verification pass cross-references generated claims against source chunks after the fact. Every response includes page-level citations back to original documents -- because your users shouldn't have to just trust the output. They should be able to verify it in 30 seconds.
What does an enterprise AI integration project cost and how long does it take?
Projects typically run $50,000 to $300,000 depending on document volume, number of LLM workflows, and how many systems we're integrating with. A standard engagement is 12-16 weeks from discovery through production deployment. But you'll have a working MVP at week 8 -- real users, real documents, real workflows -- so you can validate the approach before we harden everything for full production scale. No big reveal at the end where everyone holds their breath and hopes it works.
Can you integrate AI workflows with our existing enterprise systems like Salesforce or SAP?
Yes. The document processing pipelines are event-driven, and we use webhook-based integrations to connect downstream systems. We've built connectors for Salesforce, HubSpot, SAP, SharePoint, and plenty of custom internal tools -- if it has an API, we can wire it in. The orchestration layer triggers actions based on AI processing results: CRM record updates, approval workflows, Slack notifications, whatever the process requires. All of it with audit logging, because in regulated industries that's not optional -- that's the whole ballgame.
How do you handle sensitive enterprise data in AI processing pipelines?
Row-level security in Supabase means document access in RAG queries respects your existing permission model -- someone in the London office doesn't pull documents they shouldn't see just because they phrased a question cleverly. All data stays within your cloud infrastructure. We deploy on your AWS, GCP, or Azure accounts, not ours. For regulated industries -- healthcare, finance, legal -- we add PII detection and redaction before documents ever reach the LLM pipeline. And all API calls run under enterprise-tier provider agreements with data processing addendums already in place.
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