Turn domain knowledge into deployed specialists
Upload your data. Build datasets and evals. Choose the right optimization path. Benchmark quality. Deploy an endpoint. Expose it through API and MCP.
Pass rate
94.2%
Mean score
0.87
P95 latency
340ms
Requests
12.4k
Inference API
curl -X POST speco.ai/api/infer -H "x-api-key: sk-..." -d '{ "input": {"text": "..."}, "endpoint": "healthcare-policy" }'
Upload
Raw domain files
Dataset
Structured examples
Evals
Quality test suite
Strategy
Optimization path
Optimize
Train & refine
Benchmark
Measure quality
Deploy
Live endpoint
How it works
Six steps from raw data to deployed specialist
Each stage is a durable pipeline step. Run them individually or let Speco execute the full pipeline end-to-end.
Ingest domain data
Upload PDFs, docs, and knowledge files. Speco chunks, indexes, and prepares them for training.
Build dataset
Automatically generate structured training examples from your ingested files with quality scoring.
Generate evals
Create evaluation suites with rubrics that measure accuracy, grounding, and domain-specific quality.
Recommend strategy
Speco analyzes your data shape and recommends the optimal training path: prompt-only, RAG, SFT, or hybrid.
Optimize
Run the selected training pipeline. Fine-tune, build retrieval indexes, and generate optimized prompts.
Deploy and serve
Deploy to a live endpoint with API keys. Consume via REST API or expose as MCP tools for downstream agents.
Why Speco
The missing layer between foundation models and production
Foundation models are powerful but generic. Speco gives you the control plane to make them exceptional at your domain.
Training abstraction
Define your specialist, upload data, and Speco handles the rest. No infrastructure to manage.
Dataset control
Structured, versioned, inspectable training data. Every example traceable to its source.
Eval-first workflow
Quality is measured before deployment. Auto-generated eval suites ensure domain requirements are met.
Strategy recommendation
Speco analyzes your data and recommends whether prompt engineering, RAG, or fine-tuning is the right path.
Deployment-ready
One click from optimized model to live endpoint. API keys, usage tracking, and health monitoring included.
MCP-native
Deployed specialists are automatically available as MCP tools for downstream AI agents.
Product
Everything you need in one control plane
Specialist
Healthcare Policy Advisor
Pipeline Run
Full Pipeline — Run #47
Deployment
healthcare-policy-v3
Endpoint
speco.ai/api/infer/healthcare-policy
API Key
sk-speco-****...****7f3a
Requests
12.4k
Avg latency
340ms
Success
99.8%
MCP Integration
Agent-ready specialist
{ "tools": [ { "name": "query_policy" "description": "Query healthcare policy" } ] }
Benchmarking
Measurable, not magical
Every specialist is benchmarked before deployment. Pass rates, mean scores, latency, and per-case breakdowns give you confidence that quality is real.
Benchmark Summary
passingEval cases
142
Mean score
0.87
P95 latency
480ms
{ "mcpServers": { "speco-healthcare": { "url": "https://speco.ai/mcp/healthcare-policy" "transport": "streamable-http" } } }
query_specialist
Query with domain questions
specialist://status
Deployment health and benchmarks
analyze_with_specialist
Structured analysis template
MCP Integration
Your specialists are agent‑ready
Every deployed specialist is automatically exposed through the Model Context Protocol. Downstream AI agents can discover and use your specialists as tools, resources, and prompts.
Pricing
Simple, predictable pricing
Start free. Scale when you need to.
Free
Explore the platform. Build your first specialist.
Growth
For teams building production specialist systems.
Checkout in CAD
Enterprise
For organizations with scale and compliance requirements.
Build specialized agents from your own data.
From raw files to deployable AI systems in one control plane.