# Apex36 Technologies — Full Content Dump
# llms-full.txt: Single-fetch comprehensive view of all important site content.
# Generated for AI crawlers (GPTBot, ClaudeBot, OAI-SearchBot, PerplexityBot, ChatGPT-User).
# See /llms.txt for the index/TOC version.

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## COMPANY OVERVIEW

Apex36 Technologies is a Mumbai-based AI and software studio founded in 2022. The studio builds production AI/ML systems, RAG pipelines, data engineering platforms, SaaS products, and full-stack applications for founders and enterprises across 5+ countries. Core technical themes include RAG, semantic search, multi-LLM routing, and AI agent systems with proper evals, observability, and cost ceilings.

**Impact metrics:**
- 50+ projects delivered
- 5+ countries served
- 98% client satisfaction
- 1M+ cumulative app users across Apex36-built products
- $2M+ raised by clients Apex36 built for (DecoverAI seed round)
- 600+ UK schools running Apex36-built EdTech (an AI language tutor)

Engagements end with code and infrastructure the client's team owns and can extend, not vendor lock-in.

**Credentials:** AWS Startup Partner, Startup India registered, MSME registered.
**Contact:** office@apex36tech.com | +91 90820 75121 | Mumbai, Maharashtra, India
**Book a call:** https://cal.com/y-apex36/discovery

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## HOME PAGE — https://www.apex36tech.com/

The home page introduces Apex36 as an AI and software studio that ships production AI for product teams. The hero positions the company around "Where Innovation Meets Impact."

**Services overview (expertise grid):** AI Development, RAG, Full-Stack SaaS, Data Engineering, DevOps & Cloud Infrastructure, Experience Design.

**Featured stats:** 50+ projects delivered, 5+ countries served, 98% client satisfaction, 1M+ users, $2M+ client funding enabled.

**Featured projects on landing (first five in the portfolio):**
1. DecoverAI — Legal AI discovery, RAG on 1M+ documents, $2M+ seed raised.
2. Lang AI Tutor — Real-time voice AI language tutor, 600+ UK schools.
3. Stethy — Pharma email-triage SaaS, Phi-3.5 fine-tune, Langfuse observability.
4. BlackBox AI — Multi-agent coding platform, GCP sandboxes, live Feb 2026.
5. United Medical — German physician-staffing, Next.js monorepo, GDPR-compliant.

**CTAs on home:** View About Us (→ /about), View All Services (→ /services), full portfolio (→ /works), Free Strategy Call (→ /strategy-call), Free AI Audit (→ /audit), AI Feature Checklist (→ /ai-checklist).

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## ABOUT PAGE — https://www.apex36tech.com/about

### Company Story
Apex36 Technologies was founded in 2022 in Mumbai, India. The studio was built to ship production AI for product teams, working with early-stage startups and enterprises across 5+ countries. The name reflects a focus on measurable outcomes and precision engineering.

### Company Stats
- 50+ projects delivered
- 5+ countries served
- 98% client satisfaction
- 1M+ cumulative app users
- $2M+ in client funding enabled

### Team

**Yash Soni — Founder & CEO**
Founded Apex36 Technologies in 2022 to ship production AI for product teams. With 8+ years of experience in full-stack engineering, RAG systems, and scaling SaaS infrastructure, he works with early-stage startups and enterprises across 5+ countries.
- Experience: 8+ years
- LinkedIn: https://www.linkedin.com/in/yashsoni369/
- GitHub: https://github.com/yashsoni369
- Credentials: CKAD (Certified Kubernetes Application Developer, Cloud Native Computing Foundation); Microsoft Certified: Azure Fundamentals (Microsoft)
- Education: Master of Computer Applications (MCA), CSMU
- Knows about: AI Development, RAG, LLM Systems, Multi-LLM Routing, SaaS Architecture, Full Stack Development, Cloud Infrastructure, Vector Databases, AI Agents, Data Engineering

**Abhishek Patel — Full Stack Engineer**
Full Stack Engineer at Apex36 who leads a team building scalable, AI-integrated web applications. Specialises in OpenAI integrations, RAG-based chatbots with real-time streaming, vector search pipelines, and production SaaS features across the full stack.
- Experience: 1 yr 10 mos
- LinkedIn: https://www.linkedin.com/in/abhishek-patel18/
- Twitter/X: https://x.com/abhishekPx18
- Knows about: OpenAI Integrations, RAG, Next.js, React.js, MongoDB, TypeScript, ConvexDB, Node.js, Tailwind CSS, Framer Motion

**Raj Chauhan — Python Developer**
Python Developer at Apex36 focused on building robust, scalable backend systems using Python and Django. Works on LangChain-based AI pipeline integrations, e-commerce backends, and data-driven web applications with a strong emphasis on clean architecture and practical engineering.
- Experience: 1 yr 10 mos
- LinkedIn: https://www.linkedin.com/in/backenddeveloperrajchauhan/
- Knows about: Python, Django, LangChain, MySQL, Backend Development, JavaScript

**Raj Patel — Full Stack Engineer**
Full Stack Engineer at Apex36 specialising in AI-native products: document intelligence, semantic search, data extraction pipelines, and conversational interfaces. Has shipped production systems and delivered a US client project early in his career. Brings a deliberate approach to LLM integration, knowing where models belong versus where deterministic code should take over.
- Experience: 1 yr 5 mos
- LinkedIn: https://www.linkedin.com/in/rajpatel0369/
- Knows about: Next.js, TypeScript, React.js, Node.js, PostgreSQL, Supabase, Drizzle ORM, Zod, Vercel AI SDK, OpenAI, Claude, Google Gemini, Semantic Search, AWS S3, TanStack Query, NextAuth.js

**Tanmay Bhole — Frontend Engineer**
Frontend Engineer at Apex36 with hands-on experience across React, Next.js, and Flutter. Previously led a mobile app development team at Google Developer Student Clubs, building cross-platform applications. Oracle-certified in Generative AI, focused on crafting web and mobile experiences that solve real-world problems.
- Experience: 10 months
- LinkedIn: https://www.linkedin.com/in/tanmay-bhole-0453a7262/
- Education: B.E. Computer Science and Engineering, MGM College of Engineering and Technology
- Knows about: MERN Stack, Generative AI, Node.js, Next.js, React.js, Flutter, Python, SQL, Cloud Computing

### Values & How We Work
Apex36 starts every engagement with either a free 30-minute strategy call or a 48-hour AI integration audit. Engagements end with code and infrastructure the client's team owns and can extend. No vendor lock-in.

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## SERVICES PAGE — https://www.apex36tech.com/services

Apex36 offers six service tracks. Every engagement starts with the free strategy call or 48-hour AI audit.

### 1. AI Strategy & Roadmap
Decide what AI to build (and what not to build) before spending the budget. Pressure-test the user problem, evaluate data readiness, model cost and latency at scale, and map a sequence of features ranked by feasibility and impact.

**Deliverables:** Free 30-minute strategy call; written 48-hour AI Integration Audit on request; prioritised feature list with build/buy/skip recommendations; cost-at-scale model for the recommended AI stack.

### 2. Machine Learning, RAG & LLM Systems
Production RAG, semantic search, multi-LLM routing, and AI agents that are eval'd, observable, and cost-controlled.

**Examples:** RAG over millions of documents (DecoverAI: legal discovery on Pinecone + AWS EKS, $2M+ seed raised); multi-LLM routers that pick the right model per task; agent systems with proper evals, observability, and cost ceilings.

**Deliverables:** RAG pipeline (vector DB, retrieval, re-ranking, eval harness); multi-LLM routing with cost and latency budgets; production observability (Langfuse, OpenTelemetry, custom dashboards); hallucination guardrails and validation layers; documentation and team handoff.

**Core AI capabilities:** OpenAI / Claude / Gemini integrations, model fine-tuning, RAG, multi-LLM routing.
**Advanced AI capabilities:** AI agents, AI voice assistants, multi-agent systems with judge LLMs.
**Data and search:** Pinecone, QDrantDB, PgVector, semantic search, re-ranking.

### 3. Full-Stack SaaS Development
Production SaaS infrastructure on Next.js, Node, Python, MongoDB/Postgres, AWS/GCP, built to scale. Auth, billing, dashboards, multi-tenancy, admin tooling, and the surrounding workflows.

**Frontend:** React, Next.js, Angular, TypeScript, Tailwind CSS.
**Backend:** Python (FastAPI), Node.js (Express, NestJS), .NET Core.

**Deliverables:** Frontend (Next.js / React) and backend (Node / Python); database design (Postgres, MongoDB) with migrations; auth, billing, multi-tenancy, RBAC; CI/CD on AWS or GCP with environment isolation; performance budgets and Core Web Vitals targets.

### 4. Data Engineering & Pipelines
ETL/ELT, vector pipelines, and the data plumbing AI features need to actually work. Ingestion, normalisation, embedding generation, vector storage, refresh schedules, and quality monitors.

**Deliverables:** Ingestion pipelines (batch and streaming); embedding generation and vector store population; data quality monitors and drift detection; backfills, replays, and schema evolution support.

### 5. DevOps & Cloud Infrastructure
Cloud excellence with AWS, Azure, GCP, Docker, and Kubernetes orchestration. Robust CI/CD pipelines, infrastructure-as-code with Terraform and AWS CDK, automated deployments, and 99.9% uptime targets.

### 6. Experience Design
User-centric experiences through responsive design, intuitive UI/UX prototyping, and deep user insights.

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## WORKS PAGE — https://www.apex36tech.com/works

Full portfolio of 16 shipped projects across AI, SaaS, data engineering, and full-stack for funded startups and enterprises.

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### DecoverAI — https://www.apex36tech.com/works/decoverai-2M-seed-raised
**Tag:** Legal Tech · eDiscovery | **Year:** 2024 | **Status:** Live
**Role:** AI lead · Full build
**Metric:** $2M seed raised

**Problem:** US litigation requires reviewing hundreds of thousands to millions of documents. Traditional eDiscovery is slow, expensive (seat-based), and error-prone under deadline pressure.

**Solution:** AI-powered legal discovery platform automating document classification, privilege review, redaction, and production. RAG + semantic search pipeline over case document corpora; AI-driven classification for responsiveness and attorney-client privilege with confidence scoring; automated redaction with attorney override, Bates numbering, and privilege log generation; full audit trails on every AI decision; cloud-native scale on AWS EKS.

**Tech:** Python, Node.js, Pinecone, MongoDB, AWS EKS

**Outcomes:**
- $2M+ seed funding raised from Silicon Valley VCs
- 10+ US law firms deployed the platform
- 80% reduction in review volume
- 30,000 documents processed in 3 days; 1M+ documents supported
- $147K+ saved on one large production vs conventional review
- $60/GB per month, all-inclusive, no seat fees

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### Lang AI Tutor — https://www.apex36tech.com/works/ai-language-tutor
**Tag:** EdTech · Voice AI | **Year:** 2024 | **Status:** Live
**Role:** AI + voice pipeline
**Metric:** 10+ UK schools (now 600+ schools running the platform)

**Problem:** Duolingo-style apps build vocabulary but not speaking fluency. Real conversational practice requires a patient, always-available partner at a price point UK schools can deploy.

**Solution:** Real-time voice AI language tutor with video-avatar interface. Low-latency STT → LLM → TTS pipeline at conversational latency. AI persona gives learners a consistent tutor closer to a human than a chatbot. Conversational context retention across multi-turn sessions. Curriculum-aligned content for French, Spanish, German, and Mandarin. School subscription packaging with teacher dashboards and per-student analytics.

**Tech:** React, Node.js, MongoDB, OpenAI

**Outcomes:**
- 10+ UK schools subscribed in first 3 months; now 600+ UK schools running the platform
- Fully bootstrapped, no outside funding
- First AI-avatar tutor of its kind for UK schools

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### Stethy — https://www.apex36tech.com/works/stethy-pharma-llm-saas
**Tag:** HealthTech · Pharma | **Year:** 2024 | **Status:** Invite-only
**Role:** Platform + LLM infra
**Metric:** Pharma enterprise

**Problem:** Pharma operations teams receive high-volume internal email tied to quality events, adverse-event reports, and regulatory workflows. Off-the-shelf classifiers aren't domain-tuned; generic LLMs hallucinate on pharma jargon; regulated QMS tooling doesn't speak email.

**Solution:** Enterprise SaaS spanning eight coordinated workstreams: core workflow platform (FastAPI + SQLAlchemy + React) with Chrome extension ingesting Gmail; domain-tuned Phi-3.5 email classifier fine-tuned 2× faster via Unsloth on pharma email corpora; multi-account AWS CDK infrastructure (dev/stage/prod) with Lambda + CodePipeline; Langfuse observability hosted on Azure Container Apps via Bicep; TrackWise-compatible QMS demo; LLM RAG research using Quivr, Ragflow, LiteLLM, and AWS AgentCore.

**Tech:** FastAPI, React, AWS CDK, Langfuse, Unsloth

**Outcomes:**
- Top-tier global pharma served as invite-only enterprise clients
- Phi-3.5 fine-tune 2× faster via Unsloth, benchmarked on 9-category / 150-sample test set
- Multi-account AWS CDK: dev / stage / prod running six-repo infra fleet
- Langfuse observability on every LLM call

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### BlackBox AI — https://www.apex36tech.com/works/blackbox-ai-coding-platform
**Tag:** DevTools · Multi-Agent | **Year:** 2026 | **Status:** Live
**Role:** Multi-agent platform engineer
**Metric:** Live · Feb 2026

**Problem:** Single-agent coding assistants hit quality ceilings. Teams need multiple agents on the same task, a judge picking the best diff, execution in isolated sandboxes, and a billing layer that makes it viable as a hosted product.

**Solution:** Five-component multi-agent platform live since Feb 2026: BlackBox CLI (@blackbox_ai/blackbox-cli on npm); multi-agent runner dispatching opencode, qwen-code, and others in parallel; judge LLM comparing git diffs and picking the winner; GCP-hosted isolated Docker sandboxes on Amazon Linux 2023 with per-sandbox billing; Python LLM router with API-key management, auto-topup billing, and abuse prevention; Open Interpreter VS Code server fork and RoboCoder IDE integrations.

**Tech:** TypeScript, Node.js, Python, GCP, Docker

**Outcomes:**
- Live since Feb 2026, production multi-agent runs logged
- @blackbox_ai/blackbox-cli published on npm
- Parallel agents + judge LLM selection over git diffs
- GCP-hosted sandboxes, isolated Docker, billed per sandbox-minute

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### United Medical — https://www.apex36tech.com/works/united-medical-physician-staffing
**Tag:** HealthTech · Staffing | **Year:** 2024 | **Status:** Live
**Role:** Regulated SaaS build
**Metric:** GDPR · Germany

**Problem:** German healthcare faces chronic physician shortages. Traditional staffing relies on spreadsheets and email threads. United Medical needed a modern GDPR-bound platform at scale.

**Solution:** Four-package Next.js monorepo (um-api-next, um-scan-next, um-types-next, um-web-next) matching locum (Honorarärzte) and salaried (Festanstellung) physicians with hospitals across Germany. PostgreSQL with production-scale schema; Redis for caching and sessions; BrowserStack cross-browser QA; full German i18n; GDPR-compliant EU-region hosting. Live at unitedmed.de.

**Tech:** Next.js, TypeScript, PostgreSQL, Redis

**Outcomes:**
- Four-package Next.js monorepo with clean API / scan / types / web separation
- GDPR-bound Germany hosting, EU-region only
- Live in production, powering Nuremberg-based staffing business across German hospitals

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### Mappie AI — https://www.apex36tech.com/works/mappie-ai-pm-copilot
**Tag:** DevTools · PM AI | **Year:** 2024 | **Status:** Live
**Role:** 0→1 product build
**Metric:** 60–70% grooming saved

**Problem:** Backlog grooming is expensive. Senior PMs and engineers rewrite vague JIRA tickets into actionable stories. Generic LLM chats lack workflow context.

**Solution:** AI-powered PM copilot converting vague requirements into structured, dev-ready epics and stories. Context-aware AI chat sees the full epic/story tree without copy-paste. Inline AI editing from within the editor. Automated epic → requirements → stories pipeline. Smart prompt generation for non-prompt-engineers. Built over 12 months.

**Tech:** Next.js, Node.js, OpenAI, TipTap

**Outcomes:**
- 60–70% reduction in backlog grooming time (early users)
- 1,000 beta users; first 100 post-release get 3 months free
- Live at mappie.ai

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### Vectro AI — https://www.apex36tech.com/works/vectro-ai-sales-intelligence
**Tag:** Sales Intelligence · RevOps | **Year:** 2024 | **Status:** Live
**Role:** Data + AI platform
**Metric:** 10–15 hrs saved/mo

**Problem:** Valuable sales intelligence is buried in calls and emails. Manual CRM entry is the most hated task on every sales team. Generic call-recording tools produce transcripts, not org-specific insight.

**Solution:** Multi-channel AI platform analysing sales communications (calls, emails, Slack). Framework-driven classification (MEDDIC, BANT, SPIN) auto-populates CRM fields. Automated call scorecards, objection and pain-point tracking aggregated cross-pipeline, deal-health and seller-performance analytics, human-in-the-loop insights for PM/PMM teams. Live at thevectro.ai.

**Tech:** Node.js, TypeScript, OpenAI, Vector DB

**Outcomes:**
- 10–15 hrs saved per month per user (Ascend Head of Retail Sales testimonial)
- MEDDIC / BANT / SPIN applied automatically to CRM fields
- Multi-channel aggregation: calls, email, Slack in one intelligence pane

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### Basira — https://www.apex36tech.com/works/basira-edtech-products
**Tag:** EdTech · Multi-Product | **Year:** 2024 | **Status:** Live
**Role:** Multi-product engineering
**Metric:** 4 products shipped

**Problem:** Basira runs multiple education product lines spanning K–12, higher-ed, AI-assisted problem solving, and enterprise LLM RAG. They needed one engineering partner for heterogeneous stacks.

**Solution:**
- **PiSolved** — React AI problem-solving app with SonarQube quality gates.
- **Qoollege** — Next.js + Tailwind higher-ed platform on self-hosted Kubernetes with university data-scraping pipeline.
- **GyaanSchool** — Multi-service K–12 platform: .NET GraphQL core, Node.js microservices, two Angular frontends, Azure DevOps CI/CD.
- **Sequent** — pnpm monorepo with Python Langflow RAG sidecar using uv and a vector database on GCP for an enterprise end-client.

**Tech:** React, Next.js, Angular, .NET, Python

**Outcomes:**
- 4 products shipped under one multi-year engagement
- K–12 to enterprise coverage spanning AI problem-solving, higher-ed, and LLM RAG

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### Noetic — https://www.apex36tech.com/works/noetic-neurodivergence-screening
**Tag:** EdTech · Assessment | **Year:** 2023 | **Status:** Shipped
**Role:** Product design + build
**Metric:** 5 conditions screened

**Problem:** Diagnosing neurodevelopmental conditions involves multiple instruments, rubrics, and hand-scored reports. Clinicians need a tool that standardises assessment flow while respecting clinical judgement.

**Solution:** Digital affirmative-assessment platform covering five neurodivergence categories (ADHD, autism, dyslexia, dyspraxia, dyscalculia). Structured intake and reporting workflow. Clinician reporting views that support — not replace — clinical judgement.

**Tech:** React, Node.js

**Outcomes:**
- 5 conditions screened: ADHD, autism, dyslexia, dyspraxia, dyscalculia
- Shipped production platform replacing manual, fragmented paper workflows

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### eOxygen Insurance — https://www.apex36tech.com/works/eoxygen-mean-stack-insurance
**Tag:** Insurance · MEAN | **Year:** 2023 | **Status:** Shipped
**Role:** Full-stack build
**Metric:** MEAN stack · AWS

**Solution:** MEAN-stack insurance application on AWS covering customer records, policy data, and workflow tooling in a single full-stack deliverable.

**Tech:** MongoDB, Express, Angular, Node.js, AWS

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### KoroCRM — https://www.apex36tech.com/works/korocrm-hospitality-pms
**Tag:** Hospitality · PMS | **Year:** 2025 | **Status:** Live
**Role:** Multi-tenant platform build
**Metric:** Chivasom · Zulal live

**Problem:** Luxury-resort operations need tight coordination across housekeeping, maintenance, and guest services. Off-the-shelf PMS products are too generic for high-touch wellness resorts. Korotek needed modern, multi-tenant tooling for multiple branded properties from one codebase.

**Solution:** Multi-tenant CRM / Property Management System on NestJS 10 + Sequelize + MSSQL + Nuxt 3 / Vue 3 / Vuetify 3. Five operational modules: housekeeping, inspections, maintenance, lost & found, concierge. Custom @TenantScoped() / @TenantIds() decorators enforcing per-tenant data isolation at ORM layer. JWT + CASL authorisation. English, Thai, Arabic, French i18n. In production at Chivasom (Thailand) and Zulal (Qatar).

**Tech:** NestJS, Nuxt 3, Vue 3, MSSQL, TypeScript

**Outcomes:**
- Chivasom (Thailand) and Zulal (Qatar) live on same multi-tenant codebase
- 5 operational modules: housekeeping, inspections, maintenance, lost & found, concierge
- Per-tenant isolation via custom NestJS decorators at ORM layer

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### Water Advisor AI — https://www.apex36tech.com/works/water-advisor-ai-epa
**Tag:** Environmental · LLM | **Year:** 2026 | **Status:** Live
**Role:** AI / ML build
**Metric:** EPA CCR pipeline

**Problem:** US municipalities publish Consumer Confidence Reports (CCRs) annually, but they are long, inconsistently formatted PDFs that consumers cannot act on.

**Solution:** Address-to-CCR lookup engine resolving a user address (or ZIP) to the local water utility's latest EPA report, parsing the CCR PDF via LLM into structured PostgreSQL records (per-contaminant rows with units and limits), and returning a consumer-readable summary with treatment guidance. Every parse records model used, input tokens, output tokens, and USD cost with six-decimal precision. EWG Tap Water Database cross-reference included.

**Tech:** Next.js, TypeScript, PostgreSQL, OpenAI

**Outcomes:**
- Phase 1 MVP shipped March 2026; Phase 2 in progress April 2026
- Per-parse cost instrumentation with full auditability
- Model-agnostic parser architecture

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### SKLJ Jewellers Landing — https://www.apex36tech.com/works/sklj-jewellers-landing
**Tag:** Retail · Landing | **Year:** 2026 | **Status:** Shipped
**Role:** Landing build
**Metric:** Shipped Jan 2026

**Solution:** Single-page landing site for Soni Khimraj Lalji Jewellers with shop information, branding, and contact details.

**Tech:** TypeScript, Next.js

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## BLOG — https://www.apex36tech.com/blog

Weekly research notes from real production AI engagements. Covers RAG architectures, multi-LLM routing, Claude, GPT-5, Gemini, AI agents, and SaaS engineering patterns. Written by Yash Soni, Founder & CEO of Apex36 Technologies.

Recurring topics:
- Production RAG systems (chunking strategies, re-ranking, hybrid search, eval harnesses)
- Multi-LLM routing: when to use Claude vs GPT-5 vs Gemini, cost/latency trade-offs
- AI agents and multi-agent systems with judge LLMs
- LLM observability with Langfuse and OpenTelemetry
- SaaS architecture patterns for AI-integrated products
- Lessons from real client engagements (legal discovery, pharma, EdTech, DevTools)

**Crawl policy:** Live-search AI crawlers (GPTBot, ClaudeBot, OAI-SearchBot, PerplexityBot, ChatGPT-User) are allowed on /blog. Training-only crawlers (CCBot, anthropic-ai, cohere-ai, Google-Extended) are blocked on /blog. See /robots.txt for the full policy.

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## FREE AI STRATEGY CALL — https://www.apex36tech.com/strategy-call

**What it is:** A free 30-minute discovery call to define your AI roadmap. No pitch, no fluff, no card required, no follow-up sequence.

**In 30 minutes you'll get:** A clear picture of where AI fits in your product, what's realistic in your timeline, and what the first move is.

**Who this is for:**
- SaaS founders evaluating whether to add AI to their product
- Product teams with an AI idea but no clear architecture
- Companies that tried to build AI features and got stuck

**Stages served:** Pre-MVP / idea stage; Post-launch, pre-scale; Scaling (have users/revenue).

**How to book:** Fill in name, work email, company/product name, describe what you're trying to build or solve, and select your stage. Apex36 replies within 24 hours with a Calendly link for your preferred slot.

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## FREE AI INTEGRATION AUDIT — https://www.apex36tech.com/audit

**What it is:** A 48-hour AI feasibility assessment. Fill in 5 questions and get an honest answer on what to build, what to skip, and whether your data is ready.

**The promise:** Most teams discover the problem after they've spent 3 months building. Fill in 5 questions. Within 48 hours, you'll get a straight answer: what's viable, what's risky, and what to tackle first.

**The 5 questions:**
1. What's your product and what does it do?
2. What AI feature are you considering, or what problem do you want AI to solve?
3. What does your current tech stack look like?
4. Do you have relevant data that a model could use? (Yes — clean structured data / Some data, messy or scattered / No — starting from scratch)
5. What's your timeline and rough budget? (Discovery only / Under $10K / $10K–$50K / $50K+)

**Response:** No spam, no auto-replies. A real human reviews every submission and replies within 48 hours with an honest assessment.

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## AI FEATURE CHECKLIST — https://www.apex36tech.com/ai-checklist

**What it is:** A free downloadable PDF — "10 Questions to Ask Before Adding AI to Your SaaS." A practical checklist for founders and product teams so you don't spend 3 months building the wrong thing.

**What's inside:**
- 10 pre-build questions across strategy, data, UX, and cost
- A scoring guide to know if you're ready to build
- Honest flags for where most teams get stuck
- Free — no upsell inside the doc

Downloaded by 200+ SaaS founders.

**How to get it:** Enter first name, work email, and what you're building. One email. The PDF. Nothing else.

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## ROBOTS & CRAWLER POLICY

Live-search AI crawlers are explicitly allowed everywhere, including /blog:
- GPTBot, OAI-SearchBot, ChatGPT-User (OpenAI)
- ClaudeBot (Anthropic)
- PerplexityBot (Perplexity AI)
- Googlebot, Google-Extended (Google)
- Bingbot (Microsoft)
- DuckDuckBot (DuckDuckGo)
- facebookexternalhit (Meta)

Training-only crawlers are blocked on /blog:
- CCBot (Common Crawl)
- anthropic-ai (Anthropic training)
- cohere-ai (Cohere training)

See /robots.txt for the full, authoritative policy.

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# End of llms-full.txt
# Canonical site: https://www.apex36tech.com
# Contact: office@apex36tech.com