Agents for your most accountable functions.
AI agents are the shift from analytics-that-inform to systems-that-act. We build them where the stakes are real — project, risk, compliance — and we engineer them like infrastructure, not like demos.
For a decade we built analytics that informs decisions. The next decade belongs to systems that make them.
The era of "we built a dashboard, now waiting for someone to look at it" is over. Modern enterprises need decisions executed continuously, at the operational tempo of the business.
Foundation models made the tooling viable. The hard part now is engineering — choosing the right scope, the right guardrails, the right eval, the right human checkpoints. That's our craft.
We've shipped decision systems for over a decade. Agents are the same problem at a new abstraction. The platforms that worked before still apply: clarity of objective, rigour of measurement, discipline of deployment.
Where we ship agents
that actually run.
How we deliver.
Discovery
Two-week sprint to map the workflow, the data sources, the success criteria, and the guardrails.
Pilot
A working agent against real data in 4–6 weeks. Eval harness defined. Stakeholders piloting alongside production.
Production
Hardening, monitoring, escalation paths, and human-in-the-loop wiring. Deployed where it lives — your stack, your VPC.
Operate
Ongoing eval drift detection, prompt and tool refinement, and capability expansion as the role grows.
Agents in production
need infrastructure-grade care.
Human-in-the-loop by default
Every consequential action waits for a human approval until calibration is proven. Then graduates section by section.
Full audit trail
Every prompt, tool call, retrieval, and decision is logged. Immutable, exportable, auditor-ready.
Tool-level guardrails
Allowlist what agents can read, write, and execute. Scope tightening, rate limiting, anomaly checks at every boundary.
Eval-first
No agent ships without an eval harness — accuracy, latency, cost, fairness — and we ship the harness with the agent.
Data residency
On-prem, your VPC, or our managed cloud. Models on-host where required. Nothing leaves your perimeter.
Model-agnostic
Claude, GPT, Gemini, open-source — we pick per workload and per cost envelope, not per vendor allegiance.
Common questions, answered.
What is an AI agent in the enterprise context?
An AI agent is a software system that uses a foundation model to plan and execute multi-step workflows autonomously — reading context, calling tools, making decisions, and producing outputs that previously required human judgment. Unlike traditional analytics or RPA, agents handle unstructured information, novel situations, and tasks where the right sequence of steps is not known in advance.
How is an AI agent different from a chatbot or a workflow automation tool?
A chatbot answers questions inside a conversation. A workflow tool executes pre-defined steps. An AI agent decides which steps to take based on the situation in front of it — synthesizing context, choosing tools, and adapting its plan as conditions change. PiSquare agents are designed for high-accountability functions where this autonomy needs strict guardrails, audit trails, and human checkpoints.
What guardrails does PiSquare put around production agents?
Every agent ships with five layers of guardrails: human-in-the-loop approval on consequential actions, full audit logging of every prompt and tool call, tool-level access controls and rate limits, eval harnesses that measure accuracy and detect drift, and clear data residency boundaries (on-prem, customer VPC, or managed cloud).
How long does a PiSquare agent pilot take?
A typical agent pilot moves from discovery to a working agent in 6–8 weeks: two weeks of discovery (mapping workflow, data, success criteria, guardrails), then 4–6 weeks of build with eval-first discipline. Production hardening adds another 4–8 weeks depending on scope.
Which foundation models does PiSquare use to build agents?
PiSquare is model-agnostic. We use Claude (Anthropic), GPT (OpenAI), Gemini (Google), and open-source models (Llama, Mistral, Qwen) — chosen per workload based on capability, latency, cost, and data residency requirements. Single-vendor lock-in is explicitly avoided in our architecture.
Have a workflow that's begging
for an agent? Let's scope it.
A 30-minute discovery is enough to tell whether your use case is a 4-week pilot or a 12-week build.