Lead ML Engineer
8+ yrs deploying production ML systems; owns model selection and evaluation design.
Custom AI Development
for Products That Think, Predict, and Respond
We design and deploy production-grade AI systems — LLM integrations, RAG pipelines, AI agents, and custom ML models — built on your data, not a demo. 350+ shipped products, 40+ AI systems in production across healthcare, fintech, and retail.
OUR
IMPACT
40+
AI Systems in Production
350+
Total Products Shipped
6–10
Weeks to MVP
DEFINITION
What Is AI Development?
AI development is the process of designing, training, and deploying systems that use machine learning models or large language models (LLMs) to perform tasks that traditionally required human judgment — answering questions, recognizing patterns, predicting outcomes, or automating decisions. Unlike rule-based software, an AI system's behavior is learned from data rather than explicitly programmed line by line. In practice, this covers a spectrum from integrating an existing model (like GPT-5 or Claude) into a product, to training a custom model from scratch on proprietary data.
WHAT
WE
BUILD
AI Development Services, Not Just AI Features
Every engagement starts with a use-case audit — we don't bolt a chatbot onto your product and call it AI. Below is the full scope of what our AI development team delivers.
01
LLM Integration & Fine-Tuning
GPT-5, Claude, Gemini, and open-weight models integrated into your product, with fine-tuning on your domain data where prompting alone won't hold accuracy.
02
RAG Pipeline Development
Retrieval-augmented pipelines connecting your LLM to internal docs, databases, and knowledge bases — accurate answers grounded in your data, not the model's training set.
03
AI Agent Development
Multi-step autonomous agents that call tools, query APIs, and complete workflows — customer support resolution, internal ops automation, research agents.
04
Computer Vision
Object detection, OCR, defect inspection, and visual search built with custom-trained CV models for manufacturing, retail, and healthcare.
05
NLP & Text Intelligence
Sentiment analysis, entity extraction, document classification, and semantic search across unstructured text at scale.
06
Predictive Analytics & ML
Churn prediction, demand forecasting, and fraud detection models trained on your historical data, deployed with monitoring for drift.
07
Generative AI (Content/Media)
Text, image, and voice generation pipelines for content ops, product personalization, and creative automation.
08
AI Chatbots & Virtual Assistants
Production support and sales assistants with escalation logic, memory, and CRM integration — not a scripted decision tree.
09
MLOps & Model Deployment
CI/CD for models, versioning, A/B testing, and monitoring so performance doesn't silently degrade after launch.
WHY
TECHREFORMS
What Makes Our AI
Development Different
Development Different
Most agencies wrap a prompt around GPT and call it 'AI-powered.' Here's what we do instead.
FRAMEWORK
COMPARISON
RAG vs. Fine-Tuning — Which Fits Your AI Project?
The most common decision point in enterprise AI development. Compare on the factors that actually determine cost and accuracy.
Best for
RAG
Fast-changing knowledge bases
Fine-Tuning
Fixed domain style/behavior
Setup time
RAG
2–4 weeks
Fine-Tuning
4–8 weeks
Data required
RAG
Existing documents/DB
Fine-Tuning
Labeled training examples
Update cost
RAG
Low — update the index
Fine-Tuning
High — retrain on new data
Accuracy on niche facts
RAG
High, with citations
Fine-Tuning
Depends on training coverage
| Factor | RAG | Fine-Tuning |
|---|---|---|
| Best for | Fast-changing knowledge bases | Fixed domain style/behavior |
| Setup time | 2–4 weeks | 4–8 weeks |
| Data required | Existing documents/DB | Labeled training examples |
| Update cost | Low — update the index | High — retrain on new data |
| Accuracy on niche facts | High, with citations | Depends on training coverage |
Choose RAG if your knowledge base changes weekly and you need traceable, source-cited answers — support docs, internal wikis, compliance content.
Choose Fine-Tuning if you need a consistent tone, format, or specialized reasoning pattern that prompting can't reliably enforce.
ESTIMATE
Estimate Your AI Development Cost
Rough scope-based estimate. Final quote depends on data readiness and integration complexity.
$20K
Estimated project range (excludes ongoing model hosting/inference costs)
HOW
WE
WORK
Our AI Development Process
01
Use-Case & Feasibility Mapping
We audit your data and workflows before writing a line of code — flagging where AI adds real ROI versus where it's the wrong tool.
02
Data & Architecture Assessment
Data quality audit, vector store or fine-tuning readiness check, and system architecture design.
03
Model Selection & Build
Choosing between proprietary APIs, open-weight models, RAG, or fine-tuning based on cost, latency, and data sensitivity.
04
Integration & Evaluation
Wired into your existing product with automated evaluation sets to measure accuracy before launch, not after.
05
Deployment & Monitoring
Production deployment with drift monitoring, cost tracking, and retraining triggers.
TEAM
&
EXPERTISE
Who Builds Your AI System
Named practitioners, not an anonymous 'team of experts.' Content and estimates on this page are reviewed by the following leads.
AI Solutions Architect
Specializes in RAG pipelines and LLM integration architecture for regulated industries.
Infrastructure Lead
Owns deployment pipelines, monitoring, and drift-detection tooling post-launch.
Security & Compliance Lead
Reviews every AI engagement for HIPAA/GDPR/SOC 2 alignment before deployment.
ENGAGEMENT
MODELS
Choose How You Want to Work With Us
Fixed Price
Defined scope and deliverables upfront. Best when requirements are clear and you want budget certainty before a single sprint begins.
Sprint-Based
Pay per sprint with flexible scope. Best for evolving AI use cases where requirements shift as you validate with real users.
Ongoing Retainer
A named AI engineering team on monthly retainer — embedded in your tools, roadmap, and release cadence without hiring overhead.
Low-Risk Entry
A scoped proof-of-concept to validate feasibility, accuracy, and ROI before committing to full production build.
INDUSTRIES
AI Development Across Industries
Clinical AI
HIPAA-compliant diagnostic support, intake automation, and patient triage assistants.
Risk & Fraud AI
Real-time fraud scoring, AML/KYC automation, and compliance-ready document intelligence.
Commerce AI
Personalized recommendations, demand forecasting, and conversational search across product catalogs.
Operations AI
Internal knowledge assistants, ticket routing, and workflow automation for distributed teams.
PROOF
OF
WORK
AI Development Case Studies
Patient Intake Assistant
RAG-based intake system for a multi-clinic provider. Cut intake call volume 34%, HIPAA-compliant, deployed on private VPC.
Fraud Detection Model
Custom-trained risk model replacing rules engine. Reduced false-positive fraud flags by 41% in first quarter.
Visual Search Engine
Computer vision search for a 12K-SKU catalog. Increased add-to-cart rate 18% on search results pages.
SECURITY
&
COMPLIANCE
Enterprise-Grade AI Security
AI systems handling sensitive data are built to the same compliance bar as the rest of your infrastructure.
Private LLM Deployment
VPC-hosted or on-prem models for data that can't leave your environment.
HIPAA & BAA Support
Healthcare AI built with full audit trails and Business Associate Agreements.
SOC 2-Aligned Practices
Access controls, encryption at rest and in transit, and logging on every pipeline.
Prompt Injection Hardening
Input sanitization and guardrails on every user-facing LLM endpoint.
Data Residency Controls
Region-locked model hosting for GDPR and regional compliance requirements.
Model Output Auditing
Logged, reviewable outputs for regulated industries requiring explainability.
TECHNOLOGY
Our AI Technology Stack
LLM Providers
OpenAI GPT-4Anthropic ClaudeGoogle Gemini
OpenAI GPT-4
Anthropic Claude
Google GeminiOrchestration & RAG
LangChainLlamaIndexHugging Face
LangChain
Hugging FaceVector Databases
PineconeWeaviate
WeaviateML Frameworks
PyTorchTensorFlow
PyTorch
TensorFlowCloud AI Platforms
AWS BedrockAzure AI StudioAWS SageMakerVertex AI
AWS Bedrock
Azure AI Studio
Vertex AIRecognition
Awards & Certifications
Clutch Top AI Developer 2026AWS Select Consulting PartnerMicrosoft AI PartnerISO 27001 AlignedGoodFirms Top AI Company
Clutch Top AI Developer 2026
Microsoft AI PartnerRESOURCES
Go Deeper on Each AI Capability
Detailed breakdowns for each service area — for building out topical authority, link each card below to its own pillar page.
LLM Integration Guide
Architecture patterns and cost breakdown for integrating LLMs into existing products.
RAG vs Fine-Tuning Deep Dive
Full technical comparison beyond the summary table above.
AI Agent Architecture
How multi-step agents plan, call tools, and recover from failure.
MLOps Checklist
What production model monitoring should include before you ship.
OUR
TESTIMONIALS
Our Work
Speaks For Us
Speaks For Us
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Contact
Us
Partner with Us for
Custom AI Development
and AI That Actually Ships
Custom AI Development
and AI That Actually Ships
YourBenefits
LLM & RAG Integration
Custom ML Models
AI Agents & Automation
HIPAA & SOC 2-Aligned Builds
Full IP Ownership
Post-Launch Model Monitoring
What happens
next?
next?
We Schedule a call at your convenience
We do a discovery and consulting meeting
We prepare a proposal
FAQ
Frequently Asked
Questions
AI development is the process of designing, training, and deploying systems that use machine learning or large language models to perform tasks like answering questions, predicting outcomes, or automating decisions — as opposed to traditional rule-based software.
$8K–$15K for a chatbot or RAG assistant, $25K–$40K for a custom ML model or multi-agent system, and $60K+ for an enterprise AI platform. Final cost depends on data readiness and integration scope — see the calculator above.
RAG connects an LLM to your live data at query time, so it's cheaper to keep current and gives source-cited answers. Fine-tuning retrains the model on your data, which is better for enforcing a consistent style or specialized reasoning but costs more to update.
Both. We use proprietary APIs (OpenAI, Claude, Gemini) where speed-to-market matters, and train custom models when you need proprietary IP, on-prem deployment, or lower long-term inference cost.
A RAG-based assistant typically ships in 4–6 weeks. Custom ML models take 8–12 weeks including training and evaluation. Enterprise platforms with multiple integrations run 4–6 months.
Yes — most of our AI engagements are integrations into an existing product rather than greenfield builds. We start with an architecture assessment of your current stack before proposing the AI layer.