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AI Development Services

Build production-ready AI applications

Custom AI development for generative AI, LLM fine-tuning, RAG systems, ML models, and intelligent automation. Enterprise-grade quality at startup speed.

50+
AI Projects Delivered
99.9%
Uptime SLA
<8 wks
Avg Time to Production
24/7
Monitoring

Common Challenges

AI development pain points we solve

Stop wasting time on failed AI experiments. Build production-ready AI applications with proven architecture and engineering.

Proof-of-Concept Hell

  • POCs never reach production
  • No clear path to scale
  • Technical debt accumulates
  • Team lacks AI expertise

LLM Hallucinations

  • AI generates incorrect information
  • No source attribution
  • Unreliable outputs
  • Trust issues with stakeholders

Cost & Performance

  • API costs spiral out of control
  • Slow response times
  • Poor user experience
  • No optimization strategy

Data & Privacy

  • PII leakage concerns
  • No data governance
  • Compliance requirements unclear
  • Security vulnerabilities

Our Services

Complete AI development solutions

From concept to production, we build AI applications that scale and deliver measurable ROI.

Generative AI Applications

Build production-ready generative AI apps using GPT-4, Claude, Gemini. Content generation, chatbots, copywriting, and summarization.

  • Custom GPT-4 applications
  • Claude/Gemini integration
  • Prompt engineering
  • Output validation

RAG Systems

Retrieval-Augmented Generation systems that ground AI responses in your data. Vector databases, embeddings, semantic search.

  • Vector database setup
  • Semantic search
  • Document ingestion
  • Source attribution

LLM Fine-Tuning

Fine-tune open-source models (Llama, Mistral) on your data for domain-specific performance and cost reduction.

  • Custom dataset preparation
  • Model fine-tuning
  • Evaluation frameworks
  • Deployment optimization

AI Agents & Automation

Build autonomous AI agents that execute multi-step workflows, make decisions, and integrate with your tools.

  • LangChain/LlamaIndex
  • Tool integration
  • Multi-agent orchestration
  • Workflow automation

ML Model Development

Custom machine learning models for classification, prediction, anomaly detection, and recommendation systems.

  • Supervised learning
  • Unsupervised learning
  • Model training & tuning
  • MLOps pipeline

AI API Development

Design and build scalable AI APIs with proper authentication, rate limiting, caching, and monitoring.

  • RESTful API design
  • GraphQL endpoints
  • Authentication & security
  • Rate limiting & caching

Responsible AI & Safety

Implement guardrails, content filtering, bias detection, and compliance frameworks for ethical AI deployment.

  • Content moderation
  • Bias detection
  • PII redaction
  • Audit logging

AI Performance Optimization

Reduce latency, lower costs, and improve accuracy through model optimization, caching, and prompt engineering.

  • Prompt optimization
  • Response caching
  • Model quantization
  • Cost reduction strategies

Our Process

How we build AI applications

Build
Technical Implementation
Code changes
Create
Content Production
Write & optimize
Launch
Deploy
Go live

Case Study

Legal tech: AI-powered document analysis

The Challenge

A legal tech company needed to analyze thousands of contracts to extract key clauses, identify risks, and summarize terms. Manual review took paralegals 2-3 hours per contract. They needed an AI solution that was accurate, fast, and compliant with data privacy regulations.

Our Solution

We built a RAG system using GPT-4 with fine-tuned prompt engineering, document chunking and embedding with Pinecone vector database, semantic search for clause extraction, custom validation layer to reduce hallucinations, and PII redaction for compliance. The system processes contracts in under 2 minutes with 95% accuracy.

Results Achieved

2 minutes
Processing Time
From 2-3 hours
95%
Accuracy
Validated extraction
$500K/yr
Cost Savings
Labor cost reduction

Technology Stack

AI platforms & frameworks we use

We work with the latest AI technologies and proven production infrastructure.

LLM Providers

  • OpenAI GPT-4
  • Anthropic Claude
  • Google Gemini
  • Cohere
  • Together AI

Open Source Models

  • Llama 2/3
  • Mistral
  • Falcon
  • MPT
  • Vicuna

AI Frameworks

  • LangChain
  • LlamaIndex
  • Haystack
  • Semantic Kernel
  • AutoGen

Vector Databases

  • Pinecone
  • Weaviate
  • Qdrant
  • Milvus
  • ChromaDB

ML Frameworks

  • PyTorch
  • TensorFlow
  • Scikit-learn
  • Hugging Face
  • JAX

Infrastructure

  • AWS
  • Google Cloud
  • Azure
  • Modal
  • Replicate

Pricing

AI development packages

AI Chatbot

$3,000

Validate your AI use case

  • 2-3 week timeline
  • Working prototype
  • Architecture design
  • Data pipeline setup
  • Performance evaluation
  • Technical documentation
Most Popular

Custom AI

$6,000

Launch to production

  • 6-8 week timeline
  • Production-ready system
  • API development
  • Security & compliance
  • Monitoring & alerting
  • User interface
  • 30-day support
  • Training included

Enterprise AI

$10,000

Full-scale AI platform

  • Custom timeline
  • Multi-model architecture
  • Advanced fine-tuning
  • High-availability setup
  • SOC 2 compliance
  • Dedicated engineering team
  • Ongoing support
  • SLA guarantee

Client Testimonials

What AI clients say

“
Working with Aditya in our journey to rank our website has been a great pleasure. Aditya possesses an exceptional skill set and a deep understanding of SEO strategies and techniques. He has an uncanny ability to analyze complex data and identify critical opportunities to improve organic search rankings and drive targeted website traffic. I wholeheartedly recommend Aditya for any SEO-related position or project.
Caleb Hoon
Head of Community and Operations • OFFEO
“
We hit our KPIs in less than 3 months. Working with Aditya, we moved our key revenue-driving pages to positions #1 and #2, where we were previously ranking at #6 or #7.
James Lim
CEO • Helpling APAC
“
He helped us with market research, define the right topics along with the content brief and SEO framework. He did an extensive site audit and helped us weed out the loopholes. This helped us scale our traffic and also improved our Google ranking. Aditya was always ready to help and introduced me to a lot of concepts in SEO.
Shubhangi
Content • Adapt.io
We successfully migrated our blog from Medium to Goodnotes.com/blog without losing traffic. We also solved tech SEO problems for the Thailand, Japan, Taiwan, and Hong Kong sites, doubling the traffic with minimal efforts.
Elizabeth Ching
Marketing, Goodnotes

FAQ

AI development FAQ

We build generative AI applications (chatbots, content generation, copywriting), RAG systems for document analysis and Q&A, AI agents for workflow automation, ML models for classification and prediction, recommendation systems, sentiment analysis, anomaly detection, computer vision applications, voice AI, and custom LLM-powered tools. If it involves machine learning or large language models, we can build it.
RAG (Retrieval-Augmented Generation) grounds AI responses in your proprietary data. Instead of relying on the LLM training data (which causes hallucinations), RAG retrieves relevant documents from your knowledge base and uses them to generate accurate, source-attributed responses. Use RAG when you need AI to answer questions about your documents, product catalogs, support articles, legal contracts, or any proprietary information. RAG dramatically reduces hallucinations and improves accuracy.
Use GPT-4 (or Claude/Gemini) for general-purpose tasks, complex reasoning, and rapid prototyping. Fine-tune open-source models (Llama, Mistral) when you need domain-specific expertise, lower costs at scale (no per-token fees), data privacy (self-hosted), or specialized behavior. Fine-tuning requires training data and infrastructure but offers 10-100x cost savings at high volume. We typically start with GPT-4 for validation, then fine-tune if cost or privacy drives the decision.
We use multiple strategies: RAG systems that ground responses in source documents, prompt engineering with explicit instructions and examples, output validation that checks for factual consistency, temperature tuning to reduce creativity where accuracy matters, chain-of-thought prompting for step-by-step reasoning, source attribution so users can verify claims, and human-in-the-loop review for critical applications. Proper engineering reduces hallucinations by 80-90%.
Responsible AI ensures your system is safe, ethical, and compliant. We implement content moderation to filter harmful outputs, bias detection and mitigation, PII redaction to protect privacy, audit logging for accountability, GDPR/SOC 2 compliance frameworks, transparency features (source attribution, confidence scores), rate limiting to prevent abuse, and ethical guidelines for AI usage. Responsible AI builds trust and reduces legal/reputational risk.
POC/prototypes take 2-3 weeks. Production MVPs take 6-8 weeks. Enterprise AI platforms take 3-6 months. Timeline depends on complexity, data availability, and integration requirements. We work iteratively, delivering working software every 2 weeks, so you see progress continuously. Most clients launch a production AI feature within 2 months.
Costs include LLM API fees (OpenAI, Anthropic charge per token), vector database hosting ($50-$500/month), cloud infrastructure ($200-$2,000/month), monitoring tools ($50-$200/month), and fine-tuned model hosting if applicable. We optimize costs through prompt compression, response caching, batch processing, and switching to cheaper models for simple tasks. Typical production system costs $500-$5,000/month depending on volume. We provide cost projections during planning.
It depends. Generative AI applications using GPT-4/Claude can work with general knowledge (no custom data needed). RAG systems require your documents/knowledge base. Fine-tuned models need training data (thousands of examples). ML models need labeled datasets. We can help you collect, label, and prepare data if you do not have it yet. Many successful AI applications start with publicly available data or synthetic data generation.
Yes. We build AI features that integrate with your existing product via APIs or SDKs. We work with any tech stack (Node.js, Python, Java, .NET). Integration patterns include REST APIs for AI features, webhooks for async processing, SDKs for frontend embedding, serverless functions for event-driven AI, and microservices architecture for scalability. We ensure AI features feel native to your product, not bolted-on.
We provide monitoring dashboards showing latency, error rates, token usage, and user engagement. We set up alerting for anomalies and failures. We offer ongoing support packages for bug fixes, performance tuning, and feature additions. We document the entire system so your team can maintain it. Many clients start with a 30-day support period, then transition to monthly retainers for optimization and new features.

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Ready to Build?

Launch your AI application

Let&apos;s discuss your AI use case and design a solution that delivers measurable ROI. From POC to production in weeks, not months.

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