Build / AI Automation
Automation designed as a system: where the model helps, where it must not decide alone, and what gets logged.
TechSteps builds AI automation around a complete workflow rather than around a prompt. That means deciding which steps a model should handle, which need deterministic code, where a human has to approve, what gets logged, and what happens when the model is wrong. The automation has to be trustworthy on the day it makes a mistake, because eventually it will.
What this solves
Most AI automation projects fail in the same way. A model handles a task impressively in a demo, then meets real inputs: the malformed record, the ambiguous request, the edge case nobody described. Without a system around it, the failure is silent and someone finds out downstream.
The valuable engineering is rarely the model call. It is the process design around it. What triggers the work, what context the model gets, how the output is validated, what happens when validation fails, and who is accountable for the result.
We build the system. The model is one component in it, and usually not the most difficult one.
When people call us
If more than one of these sounds familiar, the underlying cause is often a single issue rather than several separate ones.
Technical scope
Not every engagement includes all of this. The scope is agreed in writing before we start, and anything excluded is named rather than left ambiguous.
How we approach it
High volume, well understood, and tolerant of a reviewable error rate. Automating a rare, high-stakes, poorly understood process is how these projects fail.
Language models are good at classification, extraction, summarizing and drafting. They are a poor choice for arithmetic, rule application and anything needing a guaranteed answer. Those parts should be code.
What happens when the output is wrong or the API is unavailable. If the answer is that nobody notices, the automation is not ready regardless of how well it performs.
Structured output validated against a schema, with a deterministic fallback. A model that returns something unexpected should fail loudly rather than pass bad data downstream.
For a period, both run and the outputs are compared. This produces a real error rate rather than an assumed one, and it is what makes people trust the system.
How to review, how to correct, how to monitor quality, how to disable it. Ownership sits with you, not with a black box.
What goes wrong
These are the patterns we see most often when we are called in to fix someone else's work, or our own from earlier in our careers.
Free text goes straight into a system that expected a specific shape. The failure surfaces later, in a different system, where it is much harder to trace.
Language models are unreliable at both. Anything with a right answer should be computed in code and the model used only for the parts that are genuinely linguistic.
An API times out, the automation moves on, and nobody discovers the gap until a customer complains. Errors need to be loud.
When someone asks why a record was categorized a particular way three weeks ago, there is no answer. That destroys trust faster than the error itself.
Personal or confidential information passed to an external model because nobody defined what the system is allowed to send.
Questions we actually get asked
You measure it. We run the automation alongside the existing manual process for a period and compare outputs, which produces a real error rate rather than an assumed one. That number then informs where human approval is required. Any automation sold without this step is asking you to trust a demo.
Exactly what you decide, and that decision is made explicitly before anything is built. Where data must not leave your environment, that constrains the design and we work within it. It is a requirement to design around, not an afterthought.
It depends on volume and the model chosen, and it is predictable enough to model in advance. We estimate ongoing cost as part of the design so you can judge it against the time saved before committing to a build.
The integration is built so the provider can be swapped, and the automation has to behave sensibly when the API is unavailable, which normally means queuing work rather than dropping it. Depending on a single provider with no fallback is a business risk worth designing around.
The projects that work usually remove the tedious portion of a role rather than the role. Being honest about the intent matters, because the people who understand the process are the ones whose input makes the automation correct, and they will not help if they think they are automating themselves out.
Describe the system and what is going wrong with it. A short technical conversation is usually enough for us to tell you whether this is the right work and roughly what it involves.