01AWS
Amazon Bedrock · SageMaker AI · Amazon Nova
Generative AI, enterprise knowledge systems, agentic workflows, model customization and secure cloud AI deployment.
Technology Expertise
The right technology for the problem. Not the other way around.
We combine leading AI platforms, Small Language Models, open-weight models, agent frameworks, enterprise data and AI infrastructure around the actual requirement.
Model agnosticCloud agnosticEnterprise ready
Architecture Before Allegiance
One engineering layer across a changing AI landscape.
Enterprise AI is evolving too quickly to build around a single model, cloud or technology provider. Sometimes the right answer is a managed hyperscaler service. Sometimes it is an adapted open-weight model deployed privately. Often, it is a combination.
Our role is to make those pieces work together—without turning any one product into the architecture.
Enterprise AI Platforms
Work with the cloud you already trust.
We design and deploy enterprise AI solutions across the major cloud and enterprise AI ecosystems while keeping the architecture portable.
01
02Microsoft Azure
Microsoft Foundry · Azure OpenAI · Microsoft Phi
AI systems connected naturally with enterprise data, applications, identity and Microsoft cloud infrastructure.
03Google Cloud
Vertex AI · Gemini · Gemma · Model Garden
Multimodal AI, model deployment, enterprise search, model adaptation and agentic applications.
04IBM
watsonx.ai · Granite · watsonx.data · watsonx.governance
Governed, explainable and controlled AI for enterprises operating across hybrid infrastructure.
SLMs & Open-Weight Models
Not every problem needs the largest model available.
High-volume, domain-specific, latency-sensitive and privacy-sensitive applications can benefit from smaller, focused models with greater control over customization, optimization and deployment.
Microsoft Phi
Compact models for assistants, extraction, classification, reasoning and local inference.
Google Gemma
Open-weight models for multimodal applications, document intelligence and domain adaptation.
NVIDIA Nemotron
Reasoning, agentic AI, synthetic data and tool use optimized for NVIDIA infrastructure.
Meta Llama
A broad ecosystem for private AI, RAG, enterprise agents and fine-tuning.
Mistral
Efficient models for agents, multilingual knowledge applications and private deployment.
Qwen
Reasoning, coding, multimodal, multilingual and tool-using open-model capabilities.
IBM Granite
Enterprise models for RAG, documents, extraction, classification and governed AI.
DeepSeek
Efficient open-weight options for reasoning, coding and cost-sensitive automation.
Hugging Face Ecosystem
Specialized models and tooling when the broader open-model ecosystem provides the best fit.
Why Open Weight Matters
More choice. More control.
Open-weight models give enterprises another option beyond consuming every AI capability through an external API. They can offer meaningful control—but they are not automatically the better choice.
- Deployment location
- Data processing
- Model customization
- Inference cost
- Latency
- Infrastructure
- Fine-tuning
- Portability
From base model to enterprise model
- 01Evaluate
- 02Adapt
- 03Fine-tune
- 04Optimize
- 05Deploy
- 06Monitor
Specialized Platforms & Infrastructure
Use specialist technologies where they add value.
Cohere
Useful for secure enterprise knowledge, retrieval and multilingual applications.
Uniphore
Relevant for contact centers, customer conversations and process automation.
Glean
Permission-aware search and AI across distributed enterprise applications.
NVIDIA AI Stack
Model optimization, GPU inference, AI microservices, high-throughput serving and private AI deployment.
Agentic AI & Enterprise Knowledge
A model can answer. An AI system needs to act.
Production agents need trusted context, enterprise tools, APIs, business processes, approval points and exception handling. We engineer those systems and the retrieval layer beneath them.
Single & Multi-Agent Systems
Focused agents and coordinated specialist teams built around defined work.
Tool-Enabled Agents
Secure connections to APIs, applications, databases and enterprise search.
Human-in-the-Loop
Approval, escalation and control points wherever judgment is required.
MCP Integrations
Standardized connections between AI agents, tools and contextual data.
LangChain
PostgreSQL
MongoDB
Databricks
Snowflake
Elasticsearch
Kubernetes
OllamaModel Engineering, Inference & Deployment
Selecting a model is only the beginning.
We improve model fit and production efficiency across accuracy, latency, compute, memory, throughput, security and cost—then run the workload where it makes sense.
01
Adapt
Fine-tuning · LoRA · QLoRA · PEFT · Domain adaptation
02
Optimize
Distillation · Quantization · Synthetic data · Evaluation
03
Serve
vLLM · NVIDIA Triton · TensorRT-LLM · TGI · ONNX Runtime
04
Deploy
Public cloud · Private cloud · VPC · On premises · Hybrid · Edge
Start with managed AI services. Move selected workloads into more controlled environments when security, performance or economics justify it.
Security, Governance & Observability
Production AI needs controls around the model.
Good responses are only one part of an enterprise system. AI also needs to be secure, measurable, auditable and governed. Those controls belong in the architecture from the start.
- 01PII detection and masking
- 02Prompt and response guardrails
- 03Prompt-injection protection
- 04Model evaluation
- 05Hallucination monitoring
- 06Role-based access
- 07Audit trails
- 08Data residency
- 09AI observability
- 10Token and inference monitoring
- 11Model performance
- 12Responsible AI policies
The Sovereign SLM Framework
Keep the architecture flexible as the technology changes.
One engineering foundation brings models, enterprise data, agents, applications, infrastructure and governance together.
- 01
Select
Evaluate models against the real enterprise workload, not generic benchmarks alone.
- 02
Adapt
Ground, fine-tune, distill or optimize around enterprise knowledge and requirements.
- 03
Orchestrate
Connect models with agents, applications, APIs, databases, tools and workflows.
- 04
Route
Match each workload to a model based on accuracy, complexity, latency, security and cost.
- 05
Deploy
Run across managed services, private environments, VPCs, on premises or edge.
- 06
Govern
Apply security, access, evaluation, observability and governance across the lifecycle.
- 07
Evolve
Introduce better technology without rebuilding the enterprise AI application.
Common Questions
Choosing an enterprise AI technology stack.
Are you tied to one cloud or model provider?
No. We are model agnostic and cloud agnostic. We evaluate managed services, open-weight models, SLMs and infrastructure against the workload.
When should an enterprise use a Small Language Model?
SLMs can be a strong fit for high-volume, domain-specific, latency-sensitive or privacy-sensitive workloads where focused performance and deployment control matter.
Can one AI system use several models?
Yes. Model routing can direct different workloads to different models based on accuracy, complexity, latency, security and economics.
Where can enterprise AI be deployed?
Across public cloud, private cloud, VPC, on-premises, hybrid and edge environments—depending on operational, security, data-residency and commercial requirements.
Build Your Enterprise AI Stack
Use the right model for the right workload. Keep control of everything around it.
Already evaluating models, building AI agents or considering a private AI environment? We can design the right combination of models, data, agents, infrastructure, security and governance.
Talk to Our Enterprise AI Engineering Team