AI/ML Technology Consultant

$15,000-$28,000/month retainer for AI strategy and implementation

3-4 months for LLM integration with RAG, 6-9 months for custom ML model in production

AI consultants deliver ROI through: (1) Revenue increase from AI-powered recommendations (20-30% AOV lift = $500K-$5M revenue), (2) Cost reduction through AI automation (replace 2-10 FTEs = $200K-$1M/year savings), (3) Avoided wasted AI investment ($100K-$500K spent on wrong approaches), (4) Faster time-to-market (ship AI features 3-6 months faster). Typical ROI: 5-15x within 12 months.

Overview

AI/ML consultants specialize in designing and implementing artificial intelligence and machine learning systems for real-world business applications. Whether you're integrating Large Language Models (LLMs) like GPT-4 or Claude, building custom ML models for predictions or recommendations, or implementing computer vision systems, our consultants bring practical AI expertise. We focus on AI that actually delivers business value - not research projects. Our AI consultants have implemented production ML systems processing millions of predictions daily, fine-tuned LLMs for specific domains, built data pipelines handling terabytes of training data, and deployed models that improved conversion rates by 30-50%+. We work with companies adding AI features to existing products, AI-first startups, and traditional companies transforming with AI. We specialize in practical implementations using proven technologies: OpenAI APIs, Anthropic Claude, LangChain, vector databases, PyTorch, and modern MLOps tools.

Services Offered

LLM integration and fine-tuning (GPT-4, Claude, Llama) for product features

Custom machine learning model development for predictions, recommendations, classification

RAG (Retrieval Augmented Generation) systems for enterprise knowledge bases

Computer vision implementation (object detection, image classification, OCR)

NLP and text analysis systems (sentiment analysis, entity extraction, summarization)

Data pipeline architecture for training and inference at scale

MLOps implementation for model deployment, monitoring, and retraining

AI product strategy: identifying high-value AI use cases for your product

Vector database implementation (Pinecone, Weaviate, pgvector) for semantic search

Prompt engineering and LLM optimization for cost and performance

Common Challenges We Solve

LLM responses inconsistent or hallucinating facts - can't trust for production

API costs exploding ($10K-$50K+/month) with no clear path to profitability

ML models working in notebook but failing in production

No one on team with practical ML experience - just research papers and tutorials

Data quality issues making model accuracy unacceptably low

AI features taking 6-12 months to build when promised in 2-3 months

Models degrading over time as data distribution shifts

Can't scale beyond prototype - inference costs too high

Technologies & Tools

OpenAI GPT-4 & GPT-3.5Anthropic ClaudeLangChain & LlamaIndexPinecone, Weaviate, Qdrant (vector DBs)PostgreSQL pgvectorHuggingFace TransformersPyTorch & TensorFlowMLflow (experiment tracking)Amazon SageMaker or Vertex AIModal or Replicate (inference)Python & FastAPIScikit-learn

Best Practices

Start with hosted LLM APIs (OpenAI, Anthropic) before training custom models

Implement RAG with vector databases instead of fine-tuning for knowledge retrieval

Use structured outputs (JSON mode) and function calling to reduce hallucinations

Monitor token usage and implement caching to control costs

Build comprehensive evaluation datasets before deploying models to production

Implement A/B testing infrastructure for measuring AI feature impact

Use smaller, faster models (GPT-3.5, Claude Instant) where appropriate

Design fallbacks for when AI fails - never block user workflows on AI

Typical Use Cases

Adding ChatGPT-like conversational AI to SaaS product

Building semantic search over company documentation using RAG

Implementing product recommendations to increase AOV 20-30%

Automated content generation for marketing, SEO, or product descriptions

Document processing and extraction using vision models (receipts, invoices, forms)

Customer support AI agent handling 60-80% of tickets

Lead scoring and sales prioritization using predictive models

Pricing Guidance

Hourly Rate

$250-$500/hour (AI specialists command premium rates)

Monthly Retainer

$15,000-$28,000/month retainer for AI strategy and implementation

Typical Project

$60,000-$200,000 for custom ML system development

Pricing higher for complex custom models, computer vision, or large-scale data pipelines. Lower for LLM API integration with simple RAG. Premium rates for specialized domains (healthcare AI, financial ML) requiring regulatory knowledge.

When to Hire AI/ML Technology Consultant

Hire an AI consultant when: (1) Want to add AI features to product but lack ML expertise, (2) Prototype AI system but can't get it to production-quality, (3) LLM API costs exploding unsustainably, (4) AI features promised to customers but team can't deliver, (5) Evaluating build vs buy for AI capabilities, (6) Need to audit existing AI system for improvement opportunities.

Warning Signs:

LLM integration costing $20K+/month in API fees with no path to profitability
AI features promised 6 months ago still not shipped
Model accuracy below 80% making it unsuitable for production use
No one on team who has deployed ML to production before
Spending months training custom models when APIs would work better

Case Study

Client Profile

B2B SaaS (Legal Tech)

Challenge

Legal tech SaaS company wanted to add AI-powered contract analysis to compete with larger players. Spent $180K over 9 months trying to build custom NLP models but accuracy was only 65% (needed 90%+ for lawyers to trust it). Team had no production ML experience. Burning $25K/month on OpenAI APIs without optimization. Investors pressuring for AI roadmap.

Solution

AI consultant evaluated and pivoted strategy from custom models to fine-tuned GPT-4 with RAG system. Built vector database of legal precedents using Pinecone. Implemented structured outputs with validation. Optimized prompts reducing API costs 73%. Built evaluation framework with 1,000+ test cases. Implemented caching layer. Created confidence scoring so system only shows high-confidence results. Added human-in-the-loop for edge cases.

Results

Contract analysis accuracy improved from 65% to 94%. API costs dropped from $25K/month to $6.8K/month (73% reduction). Shipped AI features in 2 months vs previous 9 months of failed attempts. 67% of users adopted AI features within first 3 months. Closed $2.4M in new enterprise contracts specifically citing AI capabilities. Investor confidence restored with clear AI roadmap.

"We wasted 9 months and $180K trying to build custom ML models. In 2 months, the AI consultant shipped production-quality features that customers love and closed millions in new business."

Completed in 3 months

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