The agent cannot reliably use company data.
Conflicting records, scattered documents, unclear permissions, and brittle integrations make every answer or action harder to trust.
Explore agent-ready dataAI Production Engineering
Your agent works. Now make the system behind it dependable.
GridArray makes the data behind agent workflows trustworthy and engineers the access, runtime, evaluation, and operations required for production.
PRODUCTION SYSTEMLIVE
01CONTEXTTrusted data
02ACCESSAPIs and MCP tools
03EXECUTEControlled agent run
04EVALUATEObservable outcome
PROVENANCETRACED
ACCESSBOUNDED
RUNSEVALUATED
Systems trusted by
Emergence AIRisevestFamasi
Engineering for real workflows, not isolated demos.The production gap
Two production gaps. One system has to close both.
An agent cannot be dependable when its context is untrustworthy or its execution is brittle. Production engineering has to address both.
The agent cannot reliably hold under production conditions.
State, retries, evaluation, observability, security, cost, and incident response become product problems as soon as a real workflow depends on the system.
Explore production agent infrastructureOne production system
The production layer behind a dependable agent workflow.
Trusted context, governed access, reliable execution, and measurable operations are connected engineering concerns, not separate services.
Trusted data & knowledge
The sources, structure, provenance, and retrieval quality one real agent workflow needs to work from dependable context.
Governed system access
Narrow APIs and MCP tools with explicit identity, permissions, validation, and auditability.
Reliable agent execution
Orchestration, state, retries, and recovery that keep a multi-step workflow controlled when real conditions differ from the demo.
Evaluation & operations
Evaluation, observability, security, cost controls, and incident handling that make the system operable after release.
How systems mature
Working workflow. Production system. Scale.
We start with the workflow you have, identify the data and execution risks under real use, and engineer the production layer around it.
01
Working workflow
The agent proves the value and gives us a real data path and execution flow to inspect.
02
Production system
We make the required context trustworthy and engineer governed access, reliability, evaluation, security, and control.
03
Scale
We harden performance, cost, infrastructure, and operations as usage grows.
Production evidence
Systems that had to work.
70%faster research workflows
Cut pharma research cycles by 70% with production AI agents
A multi-agent research system that turns fragmented scientific sources into review-ready outputs, with traceable context, orchestration, evaluation, and operational control built in.
Read case study0payment regressions
Hardened the operational flows that move real money
Senior production engineering for investment journeys where loading states, API contracts, and release discipline directly affect customer trust.
Read case studyDesigned for the hard middle
For teams with a working agent workflow and a concrete production blocker.
✓A working agent or multi-step AI workflow
✓A real user or business workflow depends on it
✓Data, system access, reliability, evaluation, scale, or operations are blocking progress
✓Technical and commercial owners can participate
Not a fit
Greenfield ideation, broad enterprise data cleanup, standalone MCP builds without a real workflow, thin AI wrappers, or generic staff augmentation.
AI production readiness call
Know what stands between your agent workflow and dependable production.
A focused diagnostic across context, access, execution, evaluation, operations, and risk. You leave with a one-page readiness scorecard.
Book a readiness call