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Next.js AI platform development

Services / AI Platform Development

Production AI Platforms — Next.js + Python + Security Built In

One engineer owns ingestion, retrieval, LLM orchestration, identity, billing, and security testing — so your platform ships without surprises. Next.js frontends with Python backends, or full-stack TypeScript. Your call.

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Typical delivery

6-8 weeks · Weekly checkpoints · One engineer owns full stack + security

Fast delivery
Ingestion, retrieval, LLM orchestration, and deployment with security testing wired in.
  • Weeks 1-2: Discovery, schema design, ingestion scaffolding
  • Weeks 3-4: Embeddings, hybrid retrieval, tenant isolation
  • Weeks 5-6: LLM orchestration, eval harness, dashboards
  • Weeks 7-8: Security testing, compliance artifacts, production cutover

What we build

RAG implementation, agent development, multi-tenant architecture, and Python backends — all inside the same sprint. No handoffs between specialists.

RAG Implementation
End-to-end RAG pipelines with ingestion, semantic chunking, hybrid retrieval, reranking, and eval harnesses.
Review the RAG architecture guide
AI Agent & MCP Development
MCP servers, tool gating, audit logging, and Anthropic/OpenAI workflows for human-in-the-loop agents.
View agent development services
Multi-tenant SaaS Architecture
Clerk orgs, Postgres RLS, SCIM provisioning, and Stripe usage ledgers so you ship platforms — not single-tenant demos.
See the multi-tenant pattern
Python Backends & Data Engineering
FastAPI services, PyTorch/LoRA fine-tuning, Celery/Temporal pipelines, and pgvector schema design for data-heavy workloads.
Read the RAG pipeline guide

RAG Implementation Deliverables

Every engagement includes the components required to harden an AI platform for launch: ingestion, hybrid retrieval, guardrails, monitoring, and security testing.

Ingestion & Semantic Chunking
pymupdf/unstructured.io extraction, LangChain semantic chunkers, OCR for scans, metadata enrichment, delta processing, and backlog monitoring.
Embedding & Storage Architecture
OpenAI text-embedding-3-large pipelines with caching, pgvector schemas keyed by tenant, versioning for re-embeds, and automated reprocessing jobs.
Hybrid Retrieval + Reranking
SQL ensembles combining vector distance + keyword scoring, Cohere/BGE rerankers, deduplication, metadata-aware filters, and PostHog telemetry.
LLM Orchestration & Guardrails
System prompt libraries, context budgeting, streaming responses, citation tracking, prompt-injection sanitizers, and policy filters.
Observability & Billing
Token + latency dashboards, eval harnesses, cost controls, Stripe usage metering, and remediation runbooks.

Process

How I Build & Secure AI Platforms

No agency relay race. One engineer owns discovery, architecture, development, and security testing so timelines don't slip and findings come with fixes.

Four-phase AI platform development process with timeline
Four-phase development: discovery, architecture, retrieval, and hardening.
  1. 01 - Discovery & audit. Review documents, security questionnaires, and target UX. Establish golden question set.
  2. 02 - Architecture & ingestion. Stand up pipelines, metadata, and background workers with monitoring.
  3. 03 - Retrieval & orchestration. Implement hybrid search, reranking, LLM prompts, and prompt-injection guardrails.
  4. 04 - Hardening & launch. Security testing, eval harnesses, handover docs, and runbooks.
Stack we ship
Modern surfaces with enterprise-grade controls.
  • Next.js 16+ / ShadCN UI / Vercel Edge
  • Python 3.12+ / FastAPI / Celery / Temporal
  • Neon or Supabase with pgvector
  • Clerk orgs, SSO/SAML, SCIM
  • Stripe Billing + usage ledgers
  • Anthropic Claude / OpenAI orchestration
  • PostHog analytics & feature flags
  • PyTorch / LoRA / ONNX for custom models
Engagement model
  • • 6-8 weeks for most builds (scales with complexity)
  • • Weekly working sessions to review progress
  • • Staging env live by Week 2
  • • Security testing baked into every sprint
  • • Cancel anytime with 2 weeks notice
View detailed pricing →

Recent builds

Proof this model works

Fintech RAG Launch

6 critical vulns patched pre-audit

  • Multi-tenant RAG had tenant isolation bug — caught during Week 4 testing.
  • Missing rate limits on inference APIs — fixed before onboarding banks.
  • Passed bank security review on the first attempt with zero findings.

Agent Operations Platform

25 workflows live, 0 regressions

  • MCP servers with tool allowlists + audit logging shipped Week 6.
  • Credential misuse caught during testing — blocked by the policy engine.
  • Adoption + cost dashboards ready for Series A investor demos.

Multi-tenant SaaS Modernization

Zero findings on external pen test

  • Rails/Next.js upgrade with RAG knowledge base and Stripe metering.
  • 800+ automated tests caught regressions before production.
  • Support backlog dropped 40% once AI summaries landed.

FAQ

Common Questions

What do engagements include?

Every build covers ingestion, embeddings, hybrid retrieval, LLM orchestration, observability, billing, and security testing. You get architecture, code, eval harnesses, and runbooks — not just a demo.

Do you handle frontend and backend?

Yes. Next.js for dashboards and customer-facing UX, Python (FastAPI/Django) for data pipelines and ML infrastructure. Same engineer, no handoffs.

How fast can you deliver production systems?

Most builds run 6-8 weeks. Weeks 1-2 cover discovery and ingestion, Weeks 3-4 nail retrieval + identity/billing, Weeks 5-6 finalize LLM orchestration, evals, and testing.

What testing coverage do you provide?

Playwright E2E tests, Vitest/pytest suites, and security testing (OWASP ZAP, prompt injection suites). We target 80%+ coverage and zero critical findings before launch.

Do you only work with Bay Area teams?

Most engagements are remote-friendly. Slack, Loom, and weekly working sessions keep teams in sync regardless of timezone.

Ready to ship your AI platform?

Whether you need an MVP, platform scale-out, or architecture review, you work directly with an AI architect who builds and secures the entire stack.

Scale existing platform Request architecture review
RAG Architecture Guide · Hybrid Search Deep Dive · RAG Pipeline Architecture

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