DemoWhy Speco
The missing layer between foundation models and production.
Foundation models are powerful, but generic. Speco gives teams the control plane to turn raw company knowledge into reliable, deployed specialist AI systems.
- 01
Training abstraction
Define your specialist, upload the data, and let Speco handle the pipeline. No infrastructure maze to manage.
- 02
Dataset control
Structured training data, versioned examples, and traceable sources — so teams can inspect what their agent is learning from.
- 03
Eval-first workflow
Quality is measured before deployment. Domain-specific eval suites help verify that specialist behavior matches real requirements.
- 04
Strategy recommendation
Speco analyzes the knowledge and recommends the right optimization path: prompting, RAG, fine-tuning, or hybrid systems.
- 05
Deployment-ready output
Move from optimized specialist to live endpoint with API keys, usage tracking, and operational visibility built in.
- 06
Native MCP layer
Deployed specialists can become MCP-ready tools for downstream agents, workflows, and internal AI systems.
Traction
Early signals from real customers.
Speco is live, paid, and finding its market. The numbers below are recent and real — not projections.
21
Production workspaces
Live customers building specialist agents on Speco.
2
Paying customers
Early MRR — first conversions came from inbound + outbound DMs.
Adapta
Enterprise deal in progress
Custom pricing, custom integration. First real enterprise contract.
100+
US-based followers
Targeted US audience inside Speco's ICP — AI builders, founders, and operators driving inbound demo conversations.
Pricing: $20/mo individual · $100/mo team · $500+/mo company. Expanding into usage-based enterprise pricing across agents, seats, API/MCP calls, storage, and reliability.
Who is João
Founder-engineer building the control plane for specialist AI.
João Víctor López Matias is the founder of Speco, an AI engineer from Ceará, Brazil, and a Computer Engineering student at Unifor. His work sits at the intersection of applied AI, product systems, and deployment-minded infrastructure — from computer vision and legal AI research to internal RAG workflows and specialist agent deployment.
Before Speco, João shipped production AI systems across multiple domains, worked on legal document classification and hybrid retrieval research, studied as a visiting student at Stanford in 2024, and built internal AI workflows at FI Group. Speco is the convergence of that path: turning raw company knowledge into reliable specialist AI systems that teams can evaluate, optimize, and deploy.
Connect
Building with Speco?
If you're shipping specialized AI, I want to talk.