Purpose-built Model Context Protocol servers and AI agents for specific business processes. Cost-aware AI that does the job without burning budget on every call.
What we deliver.
Custom MCP servers
Purpose-built Model Context Protocol servers that give agents structured access to your business systems — APIs, databases, internal tools.
Agentic workflows
Workflows that combine deterministic logic with LLM calls. Senior engineer oversight on every flow.
Cost-aware AI design
Most agentic systems burn budget by routing every call to a frontier model. We design for the cost-quality tradeoff that fits the use case.
Production-grade agents
Logging, observability, evals, fallback handling, and human-in-the-loop where it matters.
What we don’t do.
We don’t sell “AI strategy” without delivery underneath. We don’t build agentic prototypes that can’t reach production. We don’t recommend frontier-model spend when a smaller model and a structured prompt would do the same job for less.
When agentic AI isn’t the right answer for the problem, we say so.
How an engagement runs.
Use-case scoping
We assess the candidate process — value, frequency, risk, governance fit.
Architecture design
We design the agent, the MCP layer, the cost-quality model, the observability.
Build and evaluation
Senior-led build. Continuous evals. Cost and quality tracked from sprint one.
Production rollout
Phased rollout with monitoring, fallback, and clear escalation paths.
Handover and operations
Documentation, runbooks, evaluation framework — your team operates the system.
Who this is for.
CTOs and Heads of AI
Evaluating where agentic AI actually creates value — and where it would just create cost.
Heads of Innovation
Looking for a partner who can move from prototype to production without losing the engineering rigour.
Operations leaders
Looking to automate a specific high-volume process with measurable economics.
Why this team.
- AI enablers, not AI hype: We position SynlogIQ as an enabler — the team that gets you AI-ready, not the team that sells the AI dream.
- Cost-aware by design: We measure cost-per-task from sprint one. Frontier-model calls are the exception, not the default.
- Senior oversight always: Agentic systems fail in production when no one is watching. We make sure someone is.
Got a process worth automating?
Tell us the use case. We’ll tell you whether agentic AI is the right answer — and if it is, what it would cost to build.