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Enterprise / AI Integration & Automation Platform Development
Enterprise Capability

AI Integration & Automation Platform Development

Production-grade LLM orchestration and RAG pipelines that ship

CTO / VP Engineering / Head of AI at 200-5000 employee company with significant document processing or workflow automation needs
$50,000 - $300,000
137,000+
listings managed
NAS directory platform — same data pipeline patterns power RAG ingestion
91,000+
dynamic pages indexed
Content platform proving performant frontends on heavy data processing
30
languages deployed
Korean manufacturer hub — multi-tenant internationalized architecture
sub-200ms
real-time bid latency
Auction platform — same streaming architecture for LLM responses
Lighthouse 95+
performance score
Maintained across all enterprise projects including AI-powered interfaces
Architecture

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.

Waar enterprise-projecten falen

Here's the thing about building with multiple LLMs -- it sounds great in theory until you're three months in and your team has written more abstraction code than actual product features 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.
RAG pipelines that work beautifully on clean markdown docs? Pretty straightforward 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.
Most teams we talk to have made serious LLM investments but still have someone manually moving documents between systems 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.
Token costs are sneaky 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.

Wat we leveren

Multi-Provider LLM Orchestration

We build routing that doesn't care which provider it's talking to -- Claude, GPT-4o, Gemini, whatever's next. Automatic failover kicks in when a provider degrades, and prompts get adapted on the fly to match each model's instruction format. Token budgets get enforced at the user and department level. So if the marketing team has a $5,000 monthly ceiling, that ceiling actually holds. Not "holds until someone runs a batch job" -- actually holds.

Production RAG Pipeline

Single-vector search works until it doesn't -- usually right when a user searches for something that's phrased differently than how it was written in the source doc. So we combine pgvector dense search with BM25 keyword matching, then run a cross-encoder re-ranking pass to pull the most relevant chunks to the top. Generated responses include source citations. And we've got hallucination detection baked in, not bolted on after the fact as an afterthought.

Enterprise Document Processing

Documents don't arrive clean or on schedule. PDFs, Word files, emails, scanned images -- they show up in batches, out of order, inconsistently formatted. Our ingestion pipeline handles all of it with event-driven processing: classification, structured data extraction, and downstream workflow triggers that fire automatically once processing completes. No manual handoffs sitting in someone's queue waiting for them to get back from lunch.

Streaming AI Interface

The frontend is built on Next.js with the Vercel AI SDK, which gets you sub-second time-to-first-token -- users see responses starting immediately, not after a 4-second spinner. Real-time progress indicators keep people oriented during longer processing tasks. And role-based access control plugs into whatever auth provider you're already running -- Auth0, Clerk, your own homegrown system. We're not asking you to rip anything out.

Workflow Automation Engine

Multi-step AI workflows fail in interesting ways. A document processing job might hit an LLM timeout on step 3 of 7, and you need that retry to pick up exactly where it left off -- not restart from scratch and reprocess six steps you already paid for. We use Inngest or Trigger.dev for durable serverless orchestration, which means retries, observability, and clean integration with CRMs, ERPs, and notification systems are handled properly from day one. Not day 90 when something finally breaks in production.

Cost and Compliance Observability

You can't manage what you can't see. Real-time dashboards give you token usage, cost-per-query, model performance metrics, and a complete audit trail for every AI interaction. Not weekly CSV exports -- actual live visibility, per department, per workflow, per user if you need it. When something looks off, you know in minutes, not at the end of the month when the invoice lands.

Veelgestelde vragen

Hoe ga je om met failover tussen meerdere LLM-providers zoals Claude, GPT-4o en Gemini?

We bouwen een provider-agnostische orchestration layer die API-gezondheid, latency en foutpercentages in real-time monitort. Wanneer een provider degradeert of 529-fouten retourneert, worden verzoeken automatisch naar het volgende best beschikbare model gerouteerd -- met promptaanpassing om rekening te houden met de verschillen in hoe Claude versus GPT-4o versus Gemini verwacht dat instructies worden geformuleerd. Token-budgetten en kostenbeperking spelen ook een rol in die routeringsbeslissingen, niet alleen ruwe prestaties. En eerlijk gezegd? Geen handmatige interventie nodig wanneer OpenAI een slechte dinsdagochtend heeft. Je gebruikers merken het niet. Je on-call engineer wordt niet om 2 uur 's nachts gebeld. Dat is op zich al veel waard.

Welke vectordatabase beveel je aan voor enterprise RAG-pipelines?

Voor de meeste implementaties beginnen we met Supabase en pgvector -- je krijgt vectorzoeken naast je relationele queries, row-level security voor multi-tenant toegang, en één infrastructuurafhankelijkheid minder om aan je DevOps-team uit te leggen. Maar clients die miljoen documenten verwerken of sub-10ms retrieval nodig hebben, dat is een ander gesprek. Die krijgen dedicated vector stores -- Pinecone of Weaviate -- naast de primaire database. Het is geen one-size-fits-all call. Het hangt af van je werkelijke queryvolume en latency-vereisten, niet van wat indrukwekkend klinkt in een pitch deck.

Hoe reduceer je hallucinaties in RAG-aangedreven AI-reacties?

We gebruiken een aanpak met meerdere lagen omdat geen enkele techniek het alleen kan doen. Hybrid retrieval combineert dichte vectoren met BM25 keyword matching. Cross-encoder re-ranking verbetert chunk-relevantie voordat iets de LLM raakt. Systeem-prompts bevatten strikte grounding-instructies. Vervolgens voert een secundaire verificatiestap na het feit cross-references uit van gegenereerde claims tegen bron-chunks. Elke reactie bevat citaten op paginaniveau terug naar originele documenten -- omdat je gebruikers niet zomaar op de output moeten vertrouwen. Ze zouden het in 30 seconden moeten kunnen verifiëren.

Wat kosten enterprise AI integratieprojecten en hoe lang duren ze?

Projecten kosten meestal tussen $50.000 en $300.000, afhankelijk van documentvolume, aantal LLM-workflows en hoeveel systemen we integreren. Een standaard engagement duurt 12-16 weken van ontdekking tot productie-implementatie. Maar je hebt week 8 al een werkende MVP -- echte gebruikers, echte documenten, echte workflows -- zodat je de aanpak kunt valideren voordat we alles voor volledige productie-schaal hardenen. Geen grote onthulling aan het einde waar iedereen zijn adem inhoudt en hoopt dat het werkt.

Kunt u AI-workflows integreren met onze bestaande enterprise-systemen zoals Salesforce of SAP?

Ja. De document processing pipelines zijn event-driven, en we gebruiken webhook-gebaseerde integraties om downstream-systemen aan te sluiten. We hebben connectoren gebouwd voor Salesforce, HubSpot, SAP, SharePoint en veel custom interne tools -- als het een API heeft, kunnen we het erin bedraden. De orchestration layer triggert acties op basis van AI-verwerkingsresultaten: CRM-recordupdates, goedkeuringswerkflows, Slack-meldingen, wat het proces ook vereist. Allemaal met audit logging, want in gereglementeerde industrieën is dat niet optioneel -- dat is het hele spel.

Hoe ga je om met gevoelige enterprise-gegevens in AI-verwerkingspipelines?

Row-level security in Supabase betekent dat documenttoegang in RAG-queries je bestaande machtigingsmodel respecteert -- iemand op het kantoor in Londen trekt niet zomaar documenten die ze niet mogen zien omdat ze een vraag slim hebben gesteld. Alle gegevens blijven binnen je cloud-infrastructuur. We implementeren op je AWS-, GCP- of Azure-accounts, niet op de onze. Voor gereglementeerde industrieën -- gezondheidszorg, financiën, juridisch -- voegen we PII-detectie en redactie toe voordat documenten ooit de LLM-pipeline bereiken. En alle API-calls worden uitgevoerd onder enterprise-tier provideragreementen met gegevensverwerking addenda die al op hun plaats zijn.

Zie deze capaciteit in actie

NAS Equipment Directory Platform

Data pipeline and search architecture managing 137K+ listings that informed our RAG ingestion and retrieval patterns

Astrology Content Platform

91K+ dynamically generated pages proving performant Next.js frontends on top of heavy content processing pipelines

Real-Time Auction Platform

Sub-200ms streaming architecture that directly translates to low-latency LLM response delivery

Korean Manufacturer Global Hub

Multi-tenant internationalized platform across 30 languages demonstrating enterprise-scale data architecture

Headless CMS Development

Content management architecture patterns that power document ingestion and structured content delivery in AI workflows
Enterprise-engagement

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