What we deploy

Production AI systems that work inside your existing stack.

Quixas connects models, business logic, APIs, data, permissions and people into workflows that can actually operate in production.

Capability 01

Operational Workflow Deployment

Redesign high-value workflows across people, AI, rules, APIs and existing systems, with clear economics and exception handling. Most operational work is not one decision. It is a sequence: something arrives, context is gathered from several systems, a judgment is made, records are updated, and someone is told. We build that sequence as one workflow, with the AI step in its right place rather than at the centre of everything.

Orchestration across your systems

Triggers from mail, forms, events, webhooks, schedules or an existing queue, with context assembled from the systems of record you already run.

Deterministic where rules are right

If a rule can do it correctly, a rule does it. The predictable part of the workflow stays predictable and cheap.

Reasoning where judgment is required

Where a task genuinely needs reasoning across tools and documents, we build for it and bound what it is allowed to do. Autonomy is granted on evidence, in steps, never assumed at the start.

Integration architecture

Read and write boundaries agreed before build, least-privilege access, idempotent writes and safe retries against systems we do not control.

Human approval and exception paths

Approval placed where risk actually sits, escalation with an owner and a time bound, and a clear record of what the system decided versus what a person decided.

State that survives reality

Long-running work resumes after failure, restart and retry without duplicating actions in your systems.

Capability 02

Production Reliability and Evaluation

Measure whether live AI systems actually succeed, identify failure modes, and engineer the controls required for wider trust and rollout. Observability tells you the system ran. It does not tell you whether the output was right, and that gap is what keeps a working feature stuck at limited release.

Task success, measured end to end

On your real traffic, against the task the business cares about, not model answers in isolation.

Failure modes ranked by cost

Ordered by what they actually cost the operation, not by how often they occur.

A golden dataset and regression suite

Running in CI, so a quality drop is caught by the pipeline rather than by a customer.

Release gates

An explicit bar a change has to clear before it reaches production.

Review load treated as a cost

Reduced deliberately, with the evidence to justify each reduction, instead of drifting up silently as volume grows.

Permissions, latency and cost

Reviewed together with quality, because in production they are the same decision.

Capability 03

Operational Intelligence and Visibility

Expose status, exceptions, failures, bottlenecks, human actions, cost and outcomes so the operation can be managed. A workflow that runs but cannot be seen is not finished, and the people accountable for it should not have to ask an engineer what happened.

Exception queues with the reason attached

Every item the system could not finish carries why it stopped and who owns it next. That is the difference between a queue and a backlog.

Anomaly and bottleneck detection

Over your operational data, surfacing the exceptions worth acting on rather than a dashboard someone has to interrogate.

Views for the people who own the work

Throughput, pending approvals and failures, in the language of the operation rather than the language of the stack.

Alerting on business conditions

Fires on what the business cares about, not on infrastructure noise.

A record of every automated action

Available for audit, and for the review conversation that follows any incident.

Architecture

A production workflow is more than a model call.

We choose the simplest architecture that can reliably do the job. Some workflows need an agent. Some need deterministic automation. Some need several specialized components. The architecture follows the workflow, not the trend.

01

Trigger and input

Mail, form, event, webhook, schedule or queue.

02

Context and retrieval

Documents, records and history assembled from your systems.

03

AI and decision layer

Model or agent reasoning, scoped to the task.

04

Business rules

The deterministic part, kept deterministic.

05

Tools and APIs

Actions taken against real systems, with least privilege.

06

Human approval

Exception queue and escalation where risk sits.

07

System of record

The write that makes the work real.

08

Monitoring and evaluation

Success, failure, cost, latency and drift, measured continuously.

Technologies we deploy with

The stack is a means, not the proposition. We are model-agnostic and choose components per workflow, including the option of no model at all where rules do the job better.

Models and reasoning

  • Frontier and open models
  • Structured output
  • Tool use
  • Retrieval

Backend and data

  • Python and TypeScript services
  • Relational and vector storage
  • Queues and schedulers
  • Event streams

Integration

  • REST and GraphQL APIs
  • Webhooks
  • Mail and messaging
  • File and document pipelines

Operations

  • Evaluation harnesses
  • Tracing and logging
  • Dashboards and alerting
  • CI and release gates

Tell us the system or workflow that is blocking scale.

We will tell you what we would measure first, and whether it is worth deploying.

Discuss a Production Problem