The model can read the corpus. The work is pointing it at the right thing.
The model is not the strategy. We identify the decision, evidence and action that matter — then engineer the system around them.
A model call is not
a production system.
Chatbots fail when the model is allowed to improvise past the evidence. We build measured loops that retrieve context, constrain tools, test each output, correct failures and escalate uncertain cases to a person.
Seven systems. One studio.
Everything we ship is custom-built, instrumented and deployed so your team can operate it.
From brief to first production release in 14 days.
We study how your business actually runs — people, systems, data and edge cases — before choosing an architecture. Then we combine that operating context with current AI and engineering practice to build the correct system, not a generic one.
We interview operators, observe the workflow and inspect the systems, data and exceptions that shape it.
/DISCOVERY · DAYS 01–02We define the decisions, inputs, actions and boundaries — then determine where AI belongs and where deterministic code wins.
/SYSTEM-MAP · DAYS 03–04We turn real cases and failure costs into test sets, acceptance thresholds and escalation rules before the build begins.
/EVAL-DESIGN · DAY 05We implement the agents, retrieval, pipelines and interfaces against your stack in a workspace you can inspect.
/BUILD · DAYS 06–10We red-team edge cases, test permissions and fallbacks, and close reliability and latency gaps before launch.
/HARDENING · DAYS 11–12We deploy with traces, alerts and a kill switch, then stay on the pager for the first 30 days.
/PRODUCTION · DAYS 13–14/WATCH · 30dModels change. Your system should not need a rebuild.
We engineer the layer around the model so your system can adopt better models without losing its data, logic or operating history.
Security is the first constraint.
Agents touch real systems and can take real action. We design the boundary before we grant access.
Your deployment boundary
Production runs in your cloud or approved environment. Data stores and credentials remain inside that boundary.
Least privilege, reversible action
Every agent receives only the tools, records and actions required for its job. Sensitive actions require approval; kill switches can revoke execution immediately.
Auditable by default
Tool calls, retrieved sources, outputs and approvals are logged with redaction and retention controls. Your data is not used to train Recode models; external providers are configured for no-training and zero retention where supported.
The work.
Production systems, measured by what changed after launch.
The gap opens
in production.
Your competitors can access the same models. The advantage goes to the team that connects them to real data, decisions and work first.
Tell us what should stop being manual.
Send the workflow, bottleneck or decision you want fixed. An engineer replies within one working day with the next useful question.