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⚡ BESPOKE CUSTOM AI SOFTWARE & BUSINESS TOOLS

Build Your Unfair Advantage.
Proprietary AI Software & Internal Tools.

Off-the-shelf software doesn’t fit your core operational edge. We architect, train, and deploy bespoke full-stack AI web applications, internal enterprise copilots, and private RAG search engines engineered strictly for your company’s workflows with 100% intellectual property ownership.

9Techh AI Customer Support Agent 3D Visual Asset
⚡️
RAG Retrieval: < 350ms
Vector Embedding Search
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100% Private & Air-Gapped
Zero Public Model Training

THE GENERIC SOFTWARE TRAP

Why Off-The-Shelf SaaS Limits Your Scale

Pre-packaged commercial tools force your business into rigid generic boxes, compromise your proprietary data, and drain your budget with seat licenses.

Generic SaaS Feature Bloat

You pay thousands monthly for complex enterprise software suites where 90% of features are useless and the 10% you need don't fit your operational flow.

Impact: Clunky Team Workflows

Proprietary Data Security Leaks

Feeding your confidential contracts, financial models, and patent formulas into public consumer AI tools risks exposing trade secrets to third parties.

Impact: Critical IP & Legal Exposure

Zero Competitive Moat

If your agency uses the exact same software stack as every competitor, you have no technological differentiation to justify high-ticket margins.

Impact: Commodity Pricing Trap

Exponential Per-Seat Licensing

As your headcount doubles, commercial software seat taxes multiply out of control — draining capital that could fund proprietary tech assets.

Impact: Compounding Recurring SaaS Tax

ENTERPRISE AI ARCHITECTURE

4-Tier Production AI Stack. Engineered For Precision & Speed.

We combine modern full-stack web engineering with enterprise RAG pipelines, fine-tuned model weights, and deterministic guardrails.

9TECHH AI LAB // FULL-STACK ARCHITECTURE
LIVE RUNTIME
9Techh Custom AI Software Architecture Topology Diagram
FOUNDATION STACK NEXT.JS / FASTAPI
VECTOR DB PGVECTOR & PINECONE
MODELS CLAUDE 3.5 / LLAMA 3
LAYER 01 DATA INGESTION
📚

Proprietary Knowledge Ingestion & Chunking

Automated vectorization pipelines that ingest complex internal PDFs, historical databases, call transcripts, code repositories, and ERP documents.

Chunking: Semantic Hybrid Sub-Second Indexing
LAYER 02 HYBRID INFERENCE
🧠

Fine-Tuned LLMs & High-Speed RAG

Domain-adapted foundation models (Claude 3.5 Sonnet, GPT-4o, or self-hosted Llama 3) connected via dense vector embeddings for sub-350ms answer synthesis.

Retrieval: < 350ms Zero Hallucinations
LAYER 03 DETERMINISTIC SHIELD
🛡️

Strict Guardrails & Role-Based Security

Enterprise permission layers, PII anonymization proxies, and schema validation ensuring the model output strictly follows structured JSON formats.

Compliance: SOC2 / GDPR 100% PII Masking
LAYER 04 APPLICATION LAYER
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Tailored React/Next.js Web App & Mobile UI

Bespoke user interfaces engineered for your team or end customers — featuring real-time streaming tokens, rich markdown renderers, and Slack bot integrations.

Stack: Next.js + Tailwind Responsive Web & Mobile

CUSTOM AI CAPABILITIES

Software Tailored Strictly to Your Business DNA

We transform repetitive, knowledge-heavy operational bottlenecks into high-speed proprietary AI micro-products.

🤖

Internal Enterprise Copilots & SOP Engines

Empower your employees with an internal AI assistant that answers complex operational questions, pulls client records, and cites exact company SOPs in seconds.

📄 Company Knowledge Base & SOP Docs Indexed & Active
🔍 Citation Link to Source Paragraph 100% Verifiable
🔒 Private Internal Network Access Only Zero Leakage
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Client-Facing AI Micro-SaaS

Package your proprietary consulting frameworks or agency workflow into a recurring-revenue AI software tool for your clients.

Stripe Subscriptions & Auth Built-In
Multi-Tenant Database Isolated
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Automated Document Intelligence

Extract tabular data, key clauses, and financial figures from messy invoices, bank statements, and legal contracts into structured JSON.

PDF OCR to Structured JSON 99.8% Precision
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Domain-Specific Model Fine-Tuning

Fine-tune open-weights models (Llama 3, Mistral) on your company's historical datasets for distinct industry vocabulary and voice.

Private LoRA / QLoRA Tuning Client Weights
📦

100% Code & IP Ownership

You own all source code, Figma UI designs, and trained weights outright. Full Git repository transfer with zero ongoing royalties.

Full GitHub Repo Transfer 100% Owned

COMMON QUESTIONS

Frequently Asked Questions

Who owns the source code and trained weights? +
You own 100% of the intellectual property, code repository, fine-tuned weights, and UI assets. We transfer the complete GitHub repository and cloud hosting accounts directly to your organization with zero ongoing licensing fees or vendor lock-in.
Will our company data be used to train public models like OpenAI? +
Never. We use dedicated enterprise zero-retention API endpoints (OpenAI Enterprise, Anthropic Commercial, AWS Bedrock) which legally prohibit model training on customer data. For ultra-sensitive deployments, we deploy completely air-gapped open-weights models (like Llama 3) inside your private cloud.
What is the difference between RAG and Model Fine-Tuning? +
RAG (Retrieval-Augmented Generation) connects an AI model to your live company documents and databases, retrieving exact factual answers in real time without retraining. Fine-Tuning modifies the model's actual weights to teach it specialized jargon, unique formatting styles, or proprietary classification tasks. Most enterprise tools use both.
What ongoing hosting costs should we expect? +
Because we architect lightweight, serverless stacks (Next.js, FastAPI, Pinecone/pgvector), hosting costs are typically minimal ($30–$150/month for standard internal team usage). You pay cloud providers (AWS, Vercel, OpenAI) directly at raw cost with zero agency markups.
How long does it take to develop a custom AI software tool? +
Our AI Prototype Sprints are delivered in just 14 business days. Full-Scale Production AI Tools typically take between 4 to 6 weeks from initial architecture design to final live deployment and team onboarding.

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