AgentAligned

Applied AI engineering services

Book a Bottleneck Diagnostic Explore relevant use cases

From bottleneck to production system.

AgentAligned’s Forward-Deployed AI Sprint takes one operational bottleneck from diagnosis to a governed production system. Embedded engineers map the workflow, integrate the required data and tools, evaluate the system against agreed measures, and support adoption and ongoing improvement.

Forward-deployed AI engineering places builders alongside the people who own the workflow, so discovery, integration, evaluation and adoption happen within one accountable delivery cycle.

One delivery model. Three accountable phases.

Bottleneck Diagnostic

Map the current workflow, agree the baseline, identify data and system constraints, and decide whether a focused AI build is worth pursuing.

Build and Evaluation

Build the smallest production-worthy workflow, connect the required systems, and test it against representative examples and agreed acceptance thresholds.

Sustaining Production

Monitor quality, latency, cost and adoption, manage change, and improve the workflow as data, models and operations evolve.

Stage 1

01

1 to 2 weeks

Find the smallest valuable problem.

We work with the people closest to the process, map the current workflow, quantify its baseline and identify the data, integration, security and adoption constraints.

Typical outputs

  • A clear problem statement and process map
  • Baseline measures and target outcomes
  • Data and system inventory
  • Feasibility, risk and dependency assessment
  • Prioritised build recommendation
  • Go, reshape or stop decision

Stage 2

02

4 to 6 weeks for a focused first workflow

Build around the real workflow.

We create the smallest production-worthy version, connect it to the required systems, test it against representative examples and design clear approval and exception paths.

Typical outputs

  • Integration and workflow code
  • Model and tool selection based on the task
  • Representative evaluation set and acceptance thresholds
  • Human approval, escalation and fallback paths
  • Access controls, logs and operating documentation
  • User testing and launch training

Stage 3

03

Ongoing where required

Keep quality visible.

Production systems change as data, models and operations change. Where required, we monitor performance, investigate drift, update evaluations, support users and improve the workflow against agreed measures.

Typical outputs

  • Quality, latency, cost and adoption monitoring
  • Regression checks when models or prompts change
  • Incident and exception review
  • Maintenance and improvement backlog
  • Team enablement and handover

Evaluation

Reliable for the task, not impressive in a demo.

Evaluation is task-specific. There is no general score that tells you whether a system is fit for your workflow, so we build the test set from your own work and agree what acceptable looks like before anything goes live.

  1. E1Real examples
  2. E2Expected outcomes
  3. E3Acceptance thresholds
  4. E4Human review
  5. E5Production monitoring

Fit

A strong first sprint has:

Test a workflow for fit
  • 01A frequent or costly workflow
  • 02An accountable business owner
  • 03Access to subject-matter experts
  • 04Representative examples or data
  • 05A result that can be checked
  • 06A willingness to change the process, not only add a tool