Foundation Models
- OpenAI
- Anthropic
- Gemini
- Mistral
- Cohere
- Llama
STOICAI | MVP, SaaS, and Operational AI DeliveryAI systems for
AI systems for
teams shipping
MVP SaaS products
and operational automation.
We help founders, product teams, and operators design AI features, internal copilots, and workflow systems that are useful in production, not just compelling in a prototype.
MVP SaaS AIInternal copilotsWorkflow automationKnowledge and retrievalProduction delivery
Where We Fit
New AI product surfaces, internal copilots, and workflow automation
Delivery Model
Scoping, build, integration, rollout, and iteration with your team
Best Fit
Teams that need a credible path from MVP to reliable day-to-day usage
Built Across The Modern AI Stack
Models, retrieval, orchestration,
and infrastructure selected to match the job
We work across the layers commonly needed to ship AI product features, internal knowledge systems, and workflow automation without forcing a one-stack-fits-all approach.
OpenAIAnthropicGeminiMistralCohereLlamaLangChainLlamaIndexOpenAI AgentsWorkflow APIsPineconeWeaviateMilvusChromapgvectorKnowledge GraphsAzureAWSGoogle CloudDatabricksSnowflakePostgresOpenAIAnthropicGeminiMistralCohereLlamaLangChainLlamaIndexOpenAI AgentsWorkflow APIsPineconeWeaviateMilvusChromapgvectorKnowledge GraphsAzureAWSGoogle CloudDatabricksSnowflakePostgresOpenAIAnthropicGeminiMistralCohereLlamaLangChainLlamaIndexOpenAI AgentsWorkflow APIsPineconeWeaviateMilvusChromapgvectorKnowledge GraphsAzureAWSGoogle CloudDatabricksSnowflakePostgres
Orchestration
- LangChain
- LlamaIndex
- OpenAI Agents
- Workflow APIs
Retrieval
- Pinecone
- Weaviate
- Milvus
- Chroma
- pgvector
- Knowledge Graphs
Cloud & Data
- Azure
- AWS
- Google Cloud
- Databricks
- Snowflake
- Postgres
Typical EngagementsWhat We Usually Help
What We Usually Help
Teams Ship
01
AI features for SaaS products
User-facing copilots, search, assistants, and workflow steps designed to fit a real product surface.
02
Internal knowledge systems
Grounded retrieval and answer workflows built around your documents, policies, and operational context.
03
Workflow automation
AI-assisted routing, triage, summarization, and decision support for support, operations, and internal teams.
04
Operational guardrails
Prompt controls, retrieval tuning, evaluations, and approval paths that reduce avoidable production risk.
05
Data and integration fit
Architecture shaped around your stack, cloud environment, APIs, and internal ownership constraints.
06
Production handoff
Clear delivery artifacts, implementation logic, and iteration paths so the system can be maintained after launch.
How We WorkA more grounded approach
A more grounded approach
to AI delivery
We work with teams that need AI systems to fit real operating environments, not just look impressive in a sales deck or prototype.
Step 01
Prioritize the right use case
We look for workflows where AI can improve speed, consistency, or decision quality without creating unnecessary operational burden.
Step 02
Design around the operating environment
The system is shaped around your data sources, tools, permissions, review paths, and team responsibilities before build decisions are locked in.
Step 03
Refine with live usage
After rollout, we use actual team feedback to improve prompts, retrieval quality, orchestration, and reliability where it matters.
Why It MattersBuilt for repeatable use,
Built for repeatable use,
not one-off demos
Clear operational fit
Teams need outputs they can review, trust, and act on without adding confusion to existing workflows.
Lower implementation drag
Architecture and tooling choices are made with deployment realities, internal ownership, and maintenance in mind.
Systems that can mature
From early MVPs to broader internal rollouts, we build with observability, controls, and iteration paths in place.