AI that ships to production and pays for itself
We design, build and run AI systems for American businesses: large language model applications, autonomous agents, predictive machine learning and the GPU infrastructure underneath. Strategy, data, models, security and operations, handled by one team.
- LLMs, agents and classic ML
- Hosted or fully private models
- Evaluations before every release
Everything it takes to put AI to work
Most AI projects stall between a promising demo and a dependable system. Our practice is organized around closing that gap.
Agentic AI
Agents that plan, call tools and finish multi-step work across your systems, with humans approving what matters.
- Single and multi-agent workflows
- Tool and API integration, including Model Context Protocol (MCP) servers
- Approvals, audit logs, spend and rate limits
LLM applications
Assistants, search and document automation built on large language models and your own data.
- Retrieval-augmented generation (RAG) and knowledge search
- Document extraction, classification and summarization
- Customer and employee copilots
Fine-tuning and model customization
Adapt open-weight models to your domain, tone and formats when prompting is not enough.
- LoRA and QLoRA parameter-efficient tuning
- Instruction tuning and preference optimization
- Distillation into smaller, cheaper models
Predictive machine learning
Models that forecast, score and detect, trained on the data you already have.
- Demand and revenue forecasting
- Churn, risk and propensity scoring
- Anomaly and fraud detection
Computer vision
Image and video models for inspection, counting, document capture and safety.
- Defect detection and quality inspection
- OCR and form understanding
- Object detection and tracking
Language and speech
Understand and generate text and voice at scale.
- Speech-to-text and call analytics
- Sentiment, intent and topic models
- Translation and multilingual search
MLOps and LLMOps
The plumbing that keeps models accurate, fast and affordable after launch.
- Model registries, feature stores and CI/CD for models
- Evaluation sets, regression tests and A/B tests
- Monitoring for drift, quality, latency and cost
AI infrastructure and GPUs
Right-sized compute and networking for training and inference.
- GPU selection and capacity planning on NVIDIA platforms
- Inference serving with vLLM, NIM, Triton or TGI
- Private data paths with VergRail
Responsible AI and governance
Policies and controls that let you adopt AI with confidence.
- AI risk assessments aligned to the NIST AI RMF
- Red-teaming for prompt injection and data leakage
- Model documentation and human oversight design
Agents that do real work, safely
An agent is a language model that can decide which tools to use, act on the results and keep going until a task is done. Done well, agents take whole workflows off your team’s plate. Done poorly, they are expensive and risky.
We build agents the way we build any production system: narrow scope first, clear success measures, least-privilege access and full observability.
Scope
Pick a workflow with clear inputs, outputs and a measurable cost today, such as invoice exceptions or support triage.
Tools
Expose your systems through well-described, permissioned tools, often as MCP servers, so agents act through approved paths only.
Guardrails
Add approvals for risky actions, budgets for tokens and time, and checks against prompt injection.
Evaluate
Score the agent on real past cases before launch and on every change after it.
Deep experience across the AI ecosystem
We are vendor-independent. We pick tools on merit for each project and can work inside whatever stack you already run.
Foundation models
Hosted and open-weight large language models, chosen by evaluation, not hype.
- OpenAI GPT models
- Anthropic Claude
- Google Gemini
- Meta Llama
- Mistral
- DeepSeek
- Qwen
- Embedding and reranking models
Hugging Face
The open-model ecosystem for finding, tuning and serving models.
- Transformers
- Hugging Face Hub
- Datasets
- PEFT
- TRL
- Text Generation Inference
- Inference Endpoints
- Sentence Transformers
NVIDIA
GPU computing for training and high-throughput inference.
- CUDA
- NVIDIA data center GPUs
- NVIDIA NIM
- Triton Inference Server
- TensorRT-LLM
- NeMo
- RAPIDS
Cloud AI platforms
Managed AI services on the clouds you already use.
- Amazon Bedrock
- Amazon SageMaker
- Azure AI Foundry
- Azure OpenAI
- Google Vertex AI
- OCI Generative AI
Frameworks and serving
Proven libraries for building, orchestrating and serving models.
- PyTorch
- JAX
- scikit-learn
- XGBoost
- LangChain
- LangGraph
- LlamaIndex
- vLLM
- Ollama
- ONNX Runtime
- Ray
Data and retrieval
Where your knowledge lives and how models find it.
- pgvector
- Pinecone
- Weaviate
- Milvus
- Elasticsearch
- Databricks
- Snowflake
- Apache Spark
- Apache Kafka
Product names are trademarks of their owners and are listed to describe technologies we work with. Listing does not imply partnership, certification or endorsement.
Choosing how your models run
There is no single right answer. This is how we help clients decide.
| Option | Best when | Data control | Cost profile |
|---|---|---|---|
| Hosted model API | You need top quality fast and data rules allow a trusted provider | Provider terms; enterprise agreements limit retention and training | Pay per token; no fixed cost |
| Private endpoint in your cloud | You want managed models but data must stay in your cloud account | High; traffic stays in your cloud tenancy | Per token or provisioned throughput |
| Open-weight model on your GPUs | Strict data control, steady high volume, or offline needs | Full; nothing leaves your environment | GPU capacity cost; cheapest at scale |
| Fine-tuned smaller model | A narrow, repeated task where a small model can match a large one | Full when self-hosted | Training cost once, then low per request |
From idea to dependable AI in six steps
Use case and value
Rank ideas by business value, feasibility and risk. Agree the metric that defines success.
Data readiness
Find, clean and secure the data the model needs. Fix access and quality issues early.
Prototype with evaluations
Build a working prototype and an evaluation set from real cases, so quality is measured, not guessed.
Harden
Add security, guardrails, approvals, logging and failure handling.
Deploy and scale
Size GPUs or API capacity, set up serving and connect private data paths.
Operate and improve
Monitor quality, drift, latency and cost, and retrain or re-tune on a schedule.
What this looks like in practice
Typical projects we are equipped to deliver. These are illustrations of the work, not descriptions of specific clients.
Healthcare
Summarize clinical documents and prior-authorization packets with a privately hosted model, with clinicians reviewing every output.
Financial services
An agent triages anti-money-laundering alerts, gathers context from internal systems and drafts a recommendation for an analyst.
Retail
Demand forecasting by store and product, feeding replenishment, with a plain-English explanation of each forecast change.
Manufacturing
Computer vision finds surface defects on the line, and predictive models flag equipment likely to fail.
Software and SaaS
A support copilot answers from product docs and past tickets, and hands off to a person when confidence is low.
Professional services
Contract review extracts key terms, flags unusual clauses and compares them with your playbook.
Questions about AI projects
Which model should we use?
It depends on the task, your data rules and your budget. We usually test two or three candidates, including at least one hosted model and one open-weight model, against an evaluation set built from your real work, then recommend the one with the best quality for the cost.
Can you run models privately, without sending data to a third party?
Yes. We deploy open-weight models such as Llama, Mistral or Qwen on GPUs in your cloud account or data center, using serving stacks like vLLM, NVIDIA NIM or Text Generation Inference. Data never leaves your environment.
Do we need to fine-tune?
Often not. Good retrieval, prompting and tool design solve most business problems. We recommend fine-tuning when you need a consistent style or format, a smaller cheaper model, or domain knowledge that retrieval alone cannot supply.
How do you keep agents safe?
Agents get the least access they need, every tool call is logged, risky actions require human approval, and we test against prompt injection and data exfiltration before launch. Spending and rate limits stop runaway loops.
How do you measure whether AI is working?
Before building, we agree on measurable outcomes, such as handle time, accuracy against a reviewed sample or cost per task. Every release is scored on an evaluation set, and production quality is monitored continuously.
Do you resell any AI vendor?
No. We are independent and do not take referral fees from model or GPU providers, so our recommendations follow your needs.
Agentic AI in the enterprise: a practical guide
Where agents work, where they fail and how to launch one safely.
Choosing an LLM deployment: API, open weights or fine-tuning
A decision framework for quality, cost and data control.
Turn an AI idea into a working plan
In a free working session we will rank your AI ideas, check data readiness and outline a first project with a clear success measure.