End-to-End AI Engineering
At Startup Tech, we go beyond standard software development — we build intelligent, AI-powered systems that automate complex workflows, understand language, generate insights, and communicate naturally. From integrating large language models to designing full autonomous agent pipelines, we deliver AI solutions that are reliable, scalable, and production-ready.
Why AI Engineering now?
The AI landscape has shifted from experimentation to production. Businesses that deploy intelligent agents, semantic search, voice interfaces, and generative pipelines today gain a significant competitive edge. Our engineers bridge the gap between cutting-edge AI research and real-world business value.
What We Build
LLM Integration
Connect your products to OpenAI GPT-4o, Claude, Gemini, Mistral, and open-source models via Ollama — with custom prompt engineering, RAG pipelines, and fine-tuning.
Agentic AI Systems
Design and deploy autonomous AI agents that plan, reason, use tools, and complete multi-step tasks — powered by LangChain, LangGraph, CrewAI, and MCP.
VectorDB & Vector Search
Build semantic search, recommendation, and document Q&A systems using Pinecone, Weaviate, Qdrant, Chroma, and pgvector with high-precision embedding pipelines.
Speech-to-Text (SST)
Integrate real-time and batch SST using Whisper, Google Cloud Speech-to-Text, and other GCP AI APIs for voice-enabled applications and transcription services.
Text-to-Speech (TTS)
Add natural-sounding voice output with ElevenLabs, OpenAI TTS, Google Cloud TTS — for AI assistants, accessibility features, and voice agents.
FastAPI AI Backends
Build high-performance Python API backends with FastAPI, async processing, streaming responses, and robust integration layers for AI model serving.
MCP (Model Context Protocol)
Implement Anthropic's MCP standard to create universal tool interfaces that connect AI agents to your databases, APIs, and internal systems.
GCP AI APIs
Leverage Google Cloud AI: Vertex AI, Gemini API, Cloud Vision, Natural Language, Translation, Document AI, and more — integrated into your workflows.
Vibe Coding & AI-Assisted Dev
We use the latest AI-assisted development workflows — GitHub Copilot, Cursor, and custom coding agents — to ship features faster without sacrificing quality.
Generative AI (GenAI) Solutions
We build generative AI applications that create real business value — from automated content generation and intelligent document processing to custom AI copilots embedded directly in your products.
- RAG (Retrieval-Augmented Generation) — Ground LLMs in your private knowledge base for accurate, hallucination-resistant answers.
- Custom AI Copilots — Embed AI assistants into your web or desktop apps that understand your domain-specific context.
- Document Intelligence — Extract, classify, and summarise structured and unstructured documents automatically.
- AI Content Pipelines — Generate, review, and publish content at scale with human-in-the-loop oversight.
- Multimodal AI — Combine vision, text, and audio in unified AI workflows using GPT-4o, Gemini, and Claude.
Agentic AI & MCP
Agentic AI is the next frontier — AI systems that don't just answer questions, but autonomously take actions, use tools, collaborate with other agents, and complete complex long-horizon tasks. We design and deploy production-grade agentic systems using proven frameworks and the new MCP standard.
What Our Agents Can Do
- Browse the web, query databases, and call external APIs autonomously
- Write, test, and debug code using AI coding agents
- Research, summarise, and produce structured reports on demand
- Coordinate multi-agent pipelines — researcher, writer, reviewer, publisher
- Monitor systems and trigger alerts or corrective actions automatically
- Handle customer support tickets end-to-end without human escalation
MCP — Model Context Protocol
MCP is Anthropic's open standard for connecting AI models to external context sources and tools. We implement MCP servers that expose your internal systems — databases, file systems, REST APIs, CRMs — as first-class tools that any MCP-compatible AI agent (Claude, etc.) can use securely.
Our Delivery Process
Discovery & Scoping
We understand your business problem, data sources, and success criteria before recommending any AI approach.
Proof of Concept
We build a focused PoC in 1–2 weeks so you can evaluate accuracy and feasibility before committing to full development.
Production Development
Full-stack AI solution built with Python, FastAPI, your chosen LLMs, vector stores, and cloud infrastructure.
Evaluation & Safety
Rigorous evaluation of model outputs, hallucination mitigation, guardrails, and safety testing before go-live.
Deployment & Monitoring
Deploy to AWS, GCP, or Azure with observability, cost monitoring, and continuous improvement feedback loops.
Industry Use Cases
On-Premise & Open-Source LLMs with Ollama
Not every business can send data to OpenAI. We deploy and manage self-hosted, on-premise LLMs using Ollama — running Llama 3, Mistral, Phi-3, Gemma, and other open-source models on your own infrastructure for complete data privacy and compliance.
- No data leaves your infrastructure — ideal for GDPR, HIPAA, and financial regulations
- Lower long-term inference costs versus API-based models
- Customisable models fine-tuned on your proprietary data
- Hybrid deployments — local models for sensitive data, cloud APIs for general tasks
Ready to Build Your AI Solution?
Whether you need a quick PoC or a full production AI system, our engineers are ready to help. Get a free 30-minute consultation to discuss your requirements.
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