Long-running work that survives failure
AI workflows that finish, or tell you why they did not
An order, a claim or a new customer's onboarding stalls halfway, and somebody on your team finishes it by hand. Our engineers make it finish.
We automate it with AI where AI helps, such as reading the documents that come in or sorting the exceptions, and put a person's approval where a decision needs one. It's built on durable execution, so a run survives restarts, retries and partial failure, and every run leaves a history you can audit.
- Who it is for
- Operations and engineering leaders with a process that half-completes
- Bench
- 115+ engineers, averaging 15 years each
Automating a process that takes three seconds is a script. Automating one that takes three days, calls six systems, waits on a person twice and must never half-complete is a different problem, and it is the one most AI workflow projects walk into without noticing.
What we build
- Workflows on Temporal, so a run survives a deploy, a restart and a downstream outage
- Model calls as retryable steps, with the failure modes handled rather than hoped away
- Human approval steps that park a run for as long as they need to
- Compensation for the steps that cannot simply be retried, so nothing half-completes
- Workflow history as the audit trail, which is what an auditor asks for and a log file is not
- The operator console people actually use to see what is stuck and why
Clients we have done this for
Automotive / Fintech
Auto Approve
Auto Approve's loan agents were copying every refinance request by hand between the CRM, lender sites and Excel, a process that took two to three days. Our senior engineers worked hand in glove with Auto Approve's own IT staff to build the approval engine that automates it, and the company doubled the loans it processes each month without hiring more agents.
Automotive / Fintech
Location Services
A wrongful repossession, and the penalty that follows, can start with one API call nobody saw fail. Location Services' software sometimes didn't tell a vendor an assignment was closed, and nothing retried the call. Our senior engineers rebuilt that process as a durable workflow on Temporal and put it into production, so every close retries on its own, alerts the operations team when retries run out, and keeps a full history.
What makes it faster
Where a workflow has agents in it, Harness is what runs them on the Temporal path, with approvals and durability already solved. The workflow and the agents end up in one execution model rather than two that have to be kept in step.
About TechFabric HarnessFAQ
AI workflow automation: common questions
What is an AI workflow?
A business process with model calls inside it, usually long-running, usually touching several systems, usually needing a person at one or two points. Document intake, claims handling, loan origination, exception queues. The AI part is rarely the hard part. Keeping the process correct across days, restarts and partial failures is.
Why does durable execution matter here?
Because a model call fails more often than a database call, and a long process gives it many chances to.
Durable execution means the workflow remembers where it was, retries the step that failed rather than the whole run, and never half-completes an operation that touched money. TechFabric is a Temporal partner and this is the work we use it for.
Can this run alongside our existing automation?
Usually, yes. Most of these projects start by taking the one process that keeps failing rather than replacing a platform. That is also the honest way to find out whether the approach suits you before anything larger is committed.
What does the audit trail look like?
Workflow history: every step, input, output, retry and approval, in order, queryable. It is a property of how the workflow runs rather than logging somebody remembered to add, which is why it holds up when somebody asks what happened on a specific run in March.
The rest of our AI work
AI Readiness Assessment
Leaders with AI on the roadmap and no clear view of what is in the way
AI Tools Assessment
Finance and platform leaders holding a renewal they cannot justify
Agentic AI development
Engineering leaders whose agent pilot cannot get past review
AI assistants and enterprise search
IT and data leaders asked for a company assistant that can't leak
AI enablement
Engineering leaders whose team needs to own this, not outsource it
AI transformation
Executives with a mandate, a budget, and fifteen pilots