Every workflow we transformfollows the same discipline.
Most AI projects fail because they start with the model, not the process. Algen DIRECT™ is six stages that get AI-native workflows into production — deliberately, measurably, and with accountability.
Your workflows were designed for humans. AI changes what those workflows can be. We start with the process, understand what AI can now do, and redesign the workflow around it.
01 // DISCOVER
Start with the system, not the model.
GenAI fails when teams jump to prompts or models. We begin by understanding the existing workflow — every handoff, decision point, data flow, and failure mode — before any AI conversation begins.
- Map existing workflows end-to-end
- Identify every decision point and manual handoff
- Document data sources, latency expectations, and constraints
02 // IDENTIFY
Find where AI creates real leverage.
Not everything should be automated. We target the specific decisions, repetitive tasks, and bottlenecks where AI agents create genuine operational advantage — not speculative use cases.
- Score each decision point for AI leverage potential
- Separate high-value automation from low-value busywork
- Define what should stay human and why
03 // REDESIGN
Architect the new workflow around AI.
We don't bolt AI onto the old process. We redesign the workflow itself — defining clear boundaries between what's handled by agents, what's handled by existing software, and what stays with humans.
- Design agent roles with explicit boundaries
- Define human approval gates where judgment matters
- Build deterministic fallbacks for low-confidence actions
04 // ENGINEER
Build it where the work already happens.
We integrate directly into your CRMs, ticketing systems, databases, and internal tools. We don't ask teams to learn another dashboard. AI goes where the work is.
- Connect agents to existing enterprise systems
- Implement orchestration logic and tool access
- Deploy into your infrastructure, not ours
05 // CONTROL
Make it trustworthy, not just clever.
Production AI needs more than accuracy — it needs trust. We add evaluation frameworks, permission boundaries, policy enforcement, observability, and human-in-the-loop gates before anything goes live.
- Evaluate output quality before deployment
- Enforce agent permissions and policy boundaries
- Instrument with Traccia for observability and governance
06 // TRACK
Measure outcomes, not activity.
If impact cannot be measured, the system is not finished. We define baselines before AI, track deltas after deployment, and tie success to business metrics — not vanity dashboards.
- Measure reliability, latency, and failure rates
- Track cost per decision and per workflow
- Report business outcomes: time saved, accuracy gained, cost reduced