AI Automation Services
AI Automation & Development Services
We build applied AI that works in production. RAG pipelines with anti-hallucination guardrails, business process automation, LLM integrations, and AI tools grounded in your real content. Not demos. Not prototypes. Production software with measurable accuracy.
- >80% retrieval accuracy in production
- Anti-hallucination guardrails engineered in
- OpenAI, Anthropic, and open-source LLMs
- >80%
- Retrieval hit rate (relevant source in top 5)
- >70%
- Questions answered from grounded context
- <5%
- Empty context when data exists
- >0.50
- Average RAG confidence score
Production metrics from our RAG pipeline, deployed serving thousands of users.
Applied AI, not AI theatre
Most AI demos work because the questions are safe. They fail in production because real users ask unexpected things, and a hallucinating AI gives a confident wrong answer. We build AI systems engineered to fail gracefully: source-grounded, confidence-scored, with explicit fallbacks when the AI does not know something. We have done this in healthcare, where a wrong answer is a clinical risk, and in e-commerce, where a hallucinated product or price erodes customer trust immediately.
What we build
AI automation capabilities
Production AI systems, engineered for accuracy and reliability from the ground up.
RAG pipelines (Retrieval-Augmented Generation)
Production-grade RAG systems: semantic chunking, hybrid BM25 + vector retrieval, MMR reranking, and MySQL vector stores. We have hit >80% retrieval accuracy on domain-specific content in production.
AI chatbots grounded in your real content
AI that answers from what you actually know, not what it guesses. Every answer is source-cited, confidence-scored, and backed by a "not in knowledge base" fallback for queries outside your content.
Business process automation with AI
Replace manual document processing, data extraction, classification, and routing workflows with AI pipelines that run reliably at scale without human bottlenecks.
Healthcare AI (safe, source-cited, clinically appropriate)
In a clinical context, a confident wrong answer is dangerous. We build healthcare AI with strict source grounding, confidence thresholds, and anti-hallucination guardrails engineered from the start.
AI-powered product & content tools for e-commerce
Product recommendation engines, semantic search over large catalogues, automated content generation, and AI chatbots grounded in your actual product data with accurate prices and descriptions.
Custom LLM integrations (OpenAI, Anthropic, open-source)
We integrate the right model for the job: GPT-4 and GPT-4o for reasoning, Claude for long-context tasks, open-source models for on-premise requirements. We choose based on your accuracy, cost, and compliance needs.
How we work
Three ways to engage the team
Transparent rates. No lock-in. Pick the model that fits your stage.
Fixed-scope project
From $8,000
Defined AI integrations and MVPs
- Scoped and priced upfront
- Delivery in 8 to 16 weeks
- Ideal for first AI builds
- Most popular
Dedicated team
$2,500 / dev / month
Ongoing AI product development
- AI engineers on your product
- Iterate on accuracy weekly
- Scale up or down monthly
Staff augmentation
$20 / hour
Adding AI expertise to your team
- Minimum 80 hours per month
- RAG and LLM specialists
- Python and LangChain expertise
Proof of work
AI projects we have shipped
Production AI, with production metrics. Not demos or research projects.
AI Engineering · RAG · Knowledge Retrieval
Building a RAG pipeline that answers from real content, not hallucinations
>80%
Retrieval hit rate (relevant source in top 5)
>70%
Questions answered from grounded context
LaravelPHPMySQLOpenAI text-embedding-3-largeRead full case studyHealthcare · Home Health
Replacing fragmented tools with a HIPAA-compliant care coordination platform that speaks HCHB natively
7–10×
ROI generated on average
12–15 min
Saved per clinician per day
LaravelSwiftKotlinMySQLRead full case study
Technology
AI tech stack
LLMs & Embeddings
- OpenAI GPT-4
- Claude
- text-embedding-3-large
Retrieval
- MySQL vector store
- BM25 hybrid
- MMR reranking
- Pinecone
Frameworks
- LangChain
- PHP/Laravel
- Python
- Node.js
Infrastructure
- AWS
- Docker
- Redis
- PostgreSQL
Monitoring
- RAG confidence scoring
- Retrieval hit rate
- Fallback logging
FAQ
Questions about AI automation
What is RAG and why does it matter for my business?
RAG stands for Retrieval-Augmented Generation. It is the architecture that makes AI answer from your actual content instead of guessing. Without RAG, an AI will hallucinate product names, prices, and facts that do not exist. With RAG, it retrieves your real content first, then answers from it — every response is traceable to a source. For any customer-facing or business-critical AI, RAG is not optional.
Can you build AI into our existing software?
Yes. We integrate AI into existing systems — this is most of what we do. We have added RAG-powered knowledge assistants to existing Laravel and PHP platforms, integrated OpenAI APIs into live products, and built AI pipelines on top of existing MySQL databases. If you have an existing system, we can extend it with AI without a full rebuild.
How do you prevent AI hallucinations?
Through source grounding, confidence thresholds, and explicit fallbacks. Our RAG architecture retrieves your real content before generating any answer. We set a minimum confidence score (0.45 in our production deployments) below which the system returns "not in knowledge base" instead of guessing. Every answer cites its source. The LLM temperature is set to 0.1 for near-deterministic factual responses. These are not vague best practices — they are engineering decisions baked into the pipeline.
Do you use OpenAI or other models?
We use the right model for the task. GPT-4o for complex reasoning, Claude for long-context document analysis, OpenAI embeddings (text-embedding-3-large) for semantic search, and open-source models where on-premise or cost constraints require it. We are model-agnostic and recommend based on accuracy, latency, and cost requirements — not vendor preference.
Is AI automation right for my business?
It depends on the use case. AI delivers strong ROI on tasks with high volume, variable inputs, and clear success criteria — document processing, knowledge Q&A, content classification, and product recommendations. It is the wrong fit for tasks requiring judgment calls, legal liability, or real-time physical operations. Book a discovery call and we will give you an honest assessment of where AI would actually help versus where it would add complexity without value.
Start your project
Ready to build your AI system?
Tell us what you want to automate. We respond within 4 business hours.