Consulting / Apps & Workflows

Enterprise AI apps built around the work.

We begin with the operating process, its users, systems, decisions, and exceptions. The application connects the required context, permitted actions, review rules, and accountable owners.

Talk to an AI architect See our approach
A production workflow Representative scope

Context → Reasoning → Action → Review

The application combines the model with business context, permitted actions, policy checks, and human review.

Business context Tool actions Policy gates Human ownership
01For operations and product leaders 02API-first or embedded UX 03Pilot to production path

Why this layer matters

AI pilots often stop before they change the work.

Production design specifies the data sources, permitted actions, exception owners, and system-of-record integration for every workflow path.

01

Fragmented context

Knowledge, transaction data, and policies sit across systems with different permissions and owners.

02

No action layer

Answers still need to be copied into CRM, ticketing, finance, or communication tools by hand.

03

Undefined control

Teams need explicit rules for automated decisions, required reviews, and escalation.

What we deliver

One engagement, three connected workstreams.

01

Workflow & UX design

Map the job, actors, decisions, exceptions, and moments where AI can remove friction while keeping risk visible.

  • Service blueprint
  • Human/AI responsibility map
  • Prototype and acceptance criteria
02

Context & orchestration

Connect the right retrieval, tools, memory, and state into a workflow that can recover from failure.

  • Data and tool connectors
  • Agent or deterministic orchestration
  • State, retries, and fallbacks
03

Controls & delivery

Include evaluation, approvals, observability, and rollout in the first workflow that uses representative data and actual permissions.

  • Policy and permission gates
  • Evaluation harness
  • Pilot telemetry and runbook

Deployable AI app

Private Enterprise AI Chat

Employees use one company-approved workspace for AWS Bedrock and other selected model providers, with domain-restricted Google sign-in and a managed rollout.

Multi-modelGoogle SSOApproved providers
Your company AI Approved models · Private access Explore the solution ↗

Engineering enablement

OpenCode Enablement

Adopt a coding agent around approved providers, explicit tool permissions, repository-owned instructions, reusable workflows, and a measured team rollout.

Provider policyRepository guardrailsPilot rollout
>_ OpenCode Approved provider · Team policy active Explore the enablement service ↗

The engagement

Each phase answers a production question.

The initial scope is narrow. Each phase produces working software and a reviewable deliverable for the next decision.

1

Frame

Choose the decision

Define the user, workflow boundary, success measure, and unacceptable failure.

2

Build

Ship the first workflow

Deliver one complete path using representative data and actual integration constraints.

3

Prove

Run the pilot

Measure quality, adoption, latency, intervention, and cost with an accountable user group.

4

Scale

Harden the system

Expand coverage only after controls, operating ownership, and economics are understood.

What you leave with

Deployed software, test results, and an operations runbook.

The engagement includes implementation documentation and a defined handover.

01

A usable application

A focused interface or embedded experience connected to the systems where the work already happens.

02

A controlled workflow

Explicit permissions, review gates, decision records, and escalation paths for normal and exceptional cases.

03

A scale decision

Pilot results support a clear decision to invest, revise, or stop.

Typical building blocks

AgentsRAGTool callingWorkflow enginesEnterprise APIsObservability

Questions

Before we begin.

Do you build standalone apps or integrate into existing tools?

Both. We choose the lowest-friction surface for the user: a focused application when the workflow needs a new home, or an embedded experience when the system of record should remain primary.

Can we start without a fully defined AI strategy?

Yes. A bounded workflow is often the best way to clarify strategy because it reveals the real data, control, adoption, and operating constraints.

What makes the first pilot representative of production?

It uses representative data, applies actual permissions, exercises at least one meaningful integration, and measures important failure modes before broader rollout.

Bring a defined business constraint

Bring us the workflow that is stuck.

We will define a focused engagement using representative data, real permissions, and measurable success criteria.

Talk to an AI architect