AI Workflow System Dev & Design for IT Company
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AI Development Custom Software Development UX/UI Design
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Machine Learning Natural Language Processing
- Confidential
- Jan. - May 2025
- Quality
- 5.0
- Schedule
- 5.0
- Cost
- 5.0
- Willing to Refer
- 5.0
"They reproduce the issue, explain the cause in plain terms, and ship fixes in planned sprints."
- Information technology
- Italy
- 51-200 Employees
- Online Review
- Verified
Devntech developed and designed an AI workflow system with audit trails and access control for an IT company. The team's solution had a data pipeline, a governance dashboard, an API layer, and a test suite.
Devntech's system significantly reduced preparation time for model reviews and audit reports, as well as manual log checks and internal review tickets. The team delivered a stable solution that is easy to use and maintain. Devntech was responsive, knowledgeable, and skilled during the engagement.
BACKGROUND
Introduce your business and what you do there.
I'm the CEO of Domyn, an IT company that develops responsible AI solutions for regulated industries. We offer governable, composable AI architectures, including large language models (LLMs) and AI agents, supported by advanced supercomputing to help enterprises meet compliance and performance needs.
OPPORTUNITY / CHALLENGE
What specific goals or objectives did you hire Devntech to accomplish for your AI solutions?
We needed Devntech to build a secure AI workflow system with strong audit trails and access control. We also needed them to create a custom data pipeline that could handle large model inputs and rule checks. Additionally, we wanted them to develop a governance dashboard so risk and compliance teams could review AI outputs easily.
SOLUTION
What was the scope of their involvement?
Devntech has handled the full custom software build from early design to production release. They started with workshops with our AI, risk, and infrastructure teams. We mapped how our model runs, reviews, and approvals should work. After that, they built the system in modules so we could test each part early.
Key deliverables have included a custom AI job orchestration system for model runs, a secure data intake and validation layer, a full audit log for every model action and configuration change, role-based access with review and approval steps, a governance dashboard for compliance teams, an API layer for linking with our internal model tools, an admin panel for rule sets and control checks, and a test suite and staging setup for regulated clients.
They've also written clean system documentation so our internal engineers can maintain and extend the platform.
What is the team composition?
We work with a team of 6-10 from Devntech.
How did you come to work with Devntech?
We found them through an online search and the Clutch website. We chose them because their pricing fit our budget, they offered good value for the cost, and their company values aligned with ours.
What is the status of this engagement?
We worked together from January to May 2025.
RESULTS & FEEDBACK
What evidence can you share that demonstrates the impact of the engagement?
We track real numbers after going live. Our model review preparation time has dropped from about two hours to about 35 minutes per case. Our audit report preparation time has gone from a full-day task to under two hours. Our manual log checks have dropped from around 120 items per week to about 25. Our internal review tickets related to missing trace data have almost stopped.
One clear moment has stood out. During a client audit drill, we pulled a full model decision trail in minutes. Before this system, that would have required pulling logs from three tools and manual stitching.
As the client, we set clear KPIs at the start of the project, and Devntech has helped us hit or pass most of them. We track these from day one because our AI platform serves regulated sectors, so proof and trace matter.
Our main targets and achievements have been:
- AI decision trace coverage: Our target was to make every AI action traceable with full logs and review history. Before this project, only key steps were logged. After the new system, every AI job run stores full trace data. Each run shows the input source, rule checks, model version, and reviewer notes. Our audit lookup time has dropped from hours of manual digging to a few minutes using the search panel Devntech has built.
- Review and approval cycle time: We wanted faster reviews for flagged AI outputs. Our baseline was that a full review cycle often took 2–3 days with email and sheet-based tracking. After going live, most reviews now close within the same workday. Reviewers work inside one dashboard instead of four tools. Queue routing sends items to the right reviewer without manual sorting.
- Failed job recovery time: We set a target to cut how long it takes to detect and fix failed AI runs. Before, we found failures by checking logs manually. Fixing and rerunning often took half a day or more. After Devntech's build, auto alerts flag failed runs within minutes. One-click rerun works with saved configurations. Our mean recovery time has dropped to under one hour in normal cases.
- Admin workload per client workspace: We wanted each client AI workspace to need less manual setup and fewer support touches. The new workspace setup now takes about 30 minutes using templates. Support tickets tied to configuration errors have fallen sharply in the first two months. Most client teams now self-manage roles and rules using the admin panel.
We track these KPIs through system logs and admin dashboards and support desk data. Devntech has helped us define how to measure them, not just how to build the features. That has made the results easy to verify.
Another measurable result worth noting is how stable the system has stayed once it went live. After launch, we tracked system incidents, failed background jobs, and emergency fixes. In the first three months, critical incidents were very rare. No unplanned full outages occurred. Most issues were minor configuration mistakes, not code faults. Hotfix turnaround was often the same day when needed. That gave our operations and risk teams more trust in the platform.
We also measured how long it took new staff to get productive in the system. New reviewers were able to complete their first full workflow on day one. Our admin training time dropped to a single short session plus documentation. We no longer needed custom walkthrough calls for each new team.
We logged change requests and small feature additions. Many small requests shipped within one sprint. Larger feature additions had clear estimates and hit their target windows. We could plan releases with confidence because delivery stayed steady.
Before the project, we had limited visibility into AI job flow and control steps. After delivery, every job, rule check, and approval step became searchable. Audit preparation that once took days now takes a short working session. Internal audit reviews now use system exports instead of manual evidence packs.
We already covered the major KPIs earlier, but these delivery and quality metrics also show real progress. The value was not just in features shipped, but in how reliably and measurably the system improved over time.
How did Devntech perform from a project management standpoint?
Project management has been steady and simple. They work in short sprints with weekly demos. We always see real progress, not slides. When our compliance team asks for extra control points, Devntech adjusts the backlog and shows the impact on the timeline and scope.Most milestones land on the planned dates. When a task grows bigger than we first thought, they flag it early and give us clear options.
What did you find most impressive or unique about them?
They understand regulated AI work, not just code. Their team asks smart questions about audit trails, model risk, and review steps. They don't treat it like a normal app build. They treat it like control software.
Their engineers also write very readable code. Our in-house team reviews it and has no trouble following the logic.
Are there any areas they could improve?
We would have liked more sample UI mock-ups before the first build sprint. Once we asked, they added that step, and the flow improved. Aside from that, the work and teamwork have been strong.
How would you rate your overall satisfaction with the vendor?
From our client side, one last measurable point worth adding about working with Devntech is how clearly we can track progress during the build, not just after launch. We track planned versus delivered work each sprint. Most sprints close with all core tickets completed. Spillover tasks are usually low-priority items, not core features. Feature demos at the end of each sprint match the specifications we approve. That makes roadmap planning much easier for our product and risk teams.
We log defects found in QA and after release. QA defect counts drop with each test cycle. Repeat bugs are rare because fixes include added test cases. Post-release bug reports stay low and easy to trace using built-in logs.
Is there anything else the vendor did well?
We share real session recordings and notes. Devntech redesigns the rule builder with guided steps and presets. That closes the gap.
Is there anything that did not go according to plan?
From our client point of view, working with Devntech has been very positive overall, and most targets have been met. There are only a few areas where results come in a bit short at first, but they are handled well and improve in later updates.
One target is very fast load time for the audit and trace dashboard when large AI job logs are queried. In early releases, large queries take longer than we want. Power users who pull wide date ranges feel the delay.
Devntech reviews the query pattern, adds indexed search and cached views, and pushes an update. After that, load time improves, and the issue stops coming up in user feedback.
We hope non-technical risk staff can create complex rule chains on their own from day one. The first version works, but it feels too technical for some users.
System reports are correct but not client-ready in layout at the start. Teams still export and restyle them for board packs. That is below our expectation for “ready to share” output.
Devntech later adds report templates and layout options. That reduces manual rework a lot.
What stands out is their response. There's no pushback or blame. They reproduce the issue, explain the cause in plain terms, and ship fixes in planned sprints.
So yes, a few early targets are not fully met on the first pass, mostly around speed at scale and ease of use for non-technical users. None are left unresolved, and each one improves through follow-up releases.
RATINGS
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Quality
5.0Service & Deliverables
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Schedule
5.0On time / deadlines
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Cost
5.0Value / within estimates
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Willing to Refer
5.0NPS