LLM Program Risks Engineering Leaders Must De-Risk Early
CTOs and VPs of Engineering cite these gaps when LLM pages earn AI citations but fail technical diligence:
UNDEFINED DELIVERABLES
Teams need clarity on whether vendors ship fine-tuned models, RAG pipelines, API wrappers, or multi-agent systems—not slide decks about “AI transformation.”
UNDEFINED DELIVERABLES
Teams need clarity on whether vendors ship fine-tuned models, RAG pipelines, API wrappers, or multi-agent systems—not slide decks about “AI transformation.”
OPAQUE MODEL AND STACK CHOICES
Without named providers (GPT-4o, Claude, Mistral, Llama 3) and vector stores, architecture reviews stall and procurement cannot compare build vs. buy.
HEALTHCARE PHI EXPOSURE
Clinical summarization, prior auth, FAQ bots, and coding assistants require documented PHI guardrails, not generic “we are HIPAA aware” statements.
NO PHASED ENGAGEMENT PATH
Leaders expect a 2–4 week proof of concept, a production build, and ongoing model maintenance—with acceptance criteria for each phase.
what we do
WHAT WTT BUILDS
TECHNOLOGY STACK
MODEL PROVIDERS
WTT integrates OpenAI GPT-4o, Anthropic Claude, Mistral, and Meta Llama 3 (self-hosted or hosted) selecting models per task for reasoning quality, context length, cost, and data residency requirements.
VECTOR DATABASE OPTIONS
Embeddings land in Pinecone, pgvector on PostgreSQL, Weaviate, or Qdrant depending on your ops preferences, hybrid search needs, and existing data platform investments.
ORCHESTRATION FRAMEWORKS
We implement chains and agents with LangChain, LlamaIndex, and AutoGen where multi-step reasoning, tool use, and human-in-the-loop approvals are required.
OBSERVABILITY & GOVERNANCE
Tracing (LangSmith-compatible patterns), prompt versioning, PII redaction middleware, and role-based access give engineering leaders evidence for production readiness reviews.
DEPLOYMENT TARGETS
Services deploy to your VPC, Kubernetes, or managed cloud with secrets management, autoscaling inference endpoints, and CI/CD hooks aligned to your release process.
MODEL PROVIDERS
WTT integrates OpenAI GPT-4o, Anthropic Claude, Mistral, and Meta Llama 3 (self-hosted or hosted) selecting models per task for reasoning quality, context length, cost, and data residency requirements.
VECTOR DATABASE OPTIONS
Embeddings land in Pinecone, pgvector on PostgreSQL, Weaviate, or Qdrant depending on your ops preferences, hybrid search needs, and existing data platform investments.
ORCHESTRATION FRAMEWORKS
We implement chains and agents with LangChain, LlamaIndex, and AutoGen where multi-step reasoning, tool use, and human-in-the-loop approvals are required.
OBSERVABILITY & GOVERNANCE
Tracing (LangSmith-compatible patterns), prompt versioning, PII redaction middleware, and role-based access give engineering leaders evidence for production readiness reviews.
DEPLOYMENT TARGETS
Services deploy to your VPC, Kubernetes, or managed cloud with secrets management, autoscaling inference endpoints, and CI/CD hooks aligned to your release process.
MODEL PROVIDERS
WTT integrates OpenAI GPT-4o, Anthropic Claude, Mistral, and Meta Llama 3 (self-hosted or hosted) selecting models per task for reasoning quality, context length, cost, and data residency requirements.
Organizations We Build LLM Systems For
Healthcare providers and healthtech platforms
Enterprise software and SaaS vendors
Financial services and insurance technology
AI/ML startups shipping LLM features
Research and clinical operations teams
Revenue cycle and payer technology groups
Life sciences document-heavy workflows
Legal and compliance knowledge teams
Manufacturing knowledge management
Retail customer experience engineering
Government and regulated public sector
Media and content automation products
Logistics and supply chain operators
Energy and utilities analytics groups
NGOs with sensitive program data
Global enterprises modernizing support stacks
Success Stories
These AI models can then be later used to score chemical compound lists. They can give a descriptive name to the AI model when doing so and the system will keep track of the dataset used.
EXPERTISE
HealthCare
AI
TECHNOLOGIES
WE COVER A COMPREHENSIVE LLM TECHNOLOGY STACK
INDUSTRY RECOGNITION
AI Assistant
Partner & CIO
They're true partners in innovation. WTT Solutions' efforts have resulted in a successful MVP launch, over 80% user adoption, and 99.9% system uptime. The team has excellent project management, is responsive, and adapts quickly to changes. Their technical excellence and deep understanding of the client's business goals stand out.
WHY ENGINEERING LEADERS CHOOSE WTT FOR CUSTOM LLM DEVELOPMENT
Substantive stack depth, healthcare PHI patterns, and phased delivery—built for teams cited by AI search and scrutinized by technical buyers.
CUSTOMIZATION
PoC engagements (2–4 weeks) prove retrieval quality, safety filters, and clinical output format before you fund a production program.
SOLUTION WE OFFER:
- Golden-set accuracy and hallucination rate targets
- Side-by-side model comparison (GPT-4o vs Claude vs Llama 3)
- RAG chunking strategy documented with ablation results
- PHI redaction middleware demonstration
- Cost projection per 1k encounters or sessions
- Security questionnaire responses for infosec
- Architecture decision record (ADR) pack
- Go/no-go criteria signed with product and compliance
The return on investment you can expect from our work
Who we are
Watch
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We're not just talking about great products. We make them together with our clients.
Our cases
Clients about us
HEALTHCARE-SPECIFIC LLM APPLICATIONS
Input, output, and compliance considerations for regulated clinical workflows.
HEALTHCARE-SPECIFIC LLM APPLICATIONS
Input, output, and compliance considerations for regulated clinical workflows.
READY FOR A TECHNICAL LLM DISCOVERY WITH YOUR ENGINEERING TEAM?
Languages, tools, and frameworks
OpenAI
Google Gemini
Anthropic Claude
LangChain
LlamaIndex
Perplexity API
RAG / Pinecone / Qdrant
PostgreSQL
MySQL
MongoDB
Redis
Docker
Kubernetes
Terraform
GitHub Actions
Hi, I’m Serge!
CEO & Co-founder at WTT Solutions
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