What else you could do, and when you should.
Most teams evaluating AutoServa are also looking at robotic process automation, a general AI assistant, or building something in-house. Each of those is genuinely the right answer in some situations. Here is a straight comparison, including the cases where we would tell you not to hire us.
Ten questions worth asking any vendor.
Take this table into your other vendor conversations. The questions matter more than our answers to them.
| Question | RPA / rule engine | Generic AI assistant | Build in-house | AutoServa |
|---|---|---|---|---|
| Handles a case it has not seen before | No. A path it was not scripted for stops it. | Sometimes, but it answers from general knowledge rather than your rules. | Depends entirely on what your team built and how long they had. | Yes. It reasons from your decision framework and your own precedent. |
| Knows your thresholds and standards | Only the ones hard-coded into the script. | No. It has never seen your treatments, terms or tolerances. | Yes, if they were documented before the build started. | Yes. Codifying them is the first stage of the engagement. |
| Acts inside your existing systems | Yes. This is what RPA is genuinely good at. | Rarely. Most assistants reply in a chat window. | Yes, with the integration work that implies. | Yes. APIs where they exist, the browser where they do not. |
| Survives a change to the underlying screen | No. Selector changes are the classic RPA maintenance cost. | Not applicable, it is not driving your systems. | Depends on how the integration was written. | Yes. It reads the screen the way a person does rather than by fixed selectors. |
| Shows why it decided something | It shows what it did, not why. There was no why. | It can produce an explanation, which may or may not be the real reason. | Only if evidence logging was built in deliberately. | Yes. Evidence trail on every action is part of the product. |
| Enforces approval chains and spending limits | Only where scripted, and it is easy to miss a path. | No. | Yes, if built. This is where in-house projects usually run long. | Yes. Workflow Guard checks policy before any action executes. |
| Keeps expertise when a key person leaves | No. The script encodes steps, not judgement. | No. | Partly. The code holds rules; the reasoning behind them usually is not captured. | Yes. Consolidating that judgement is the point of the engagement. |
| Time to a live, supervised run | Weeks per process, then ongoing repair. | Immediate, but it is not running your queue. | Commonly six to twelve months for a first workflow. | Typically five to eight weeks for the first queue. |
| What it costs you later | Maintenance. Scripts break whenever a system changes. | Low, because it is doing correspondingly little. | Your engineers own it forever, alongside their other work. | Model costs scale with volume; the framework is yours to keep. |
| Where it genuinely wins | High-volume, totally stable, zero-judgement data entry. | Drafting, summarising and answering general questions. | Deeply proprietary logic you would never hand to a vendor. | Repetitive work where judgement decides the outcome. |
Scroll the table sideways to see every column. The last row is the one we would read first if we were buying.
Four times we will tell you not to hire us.
A decision audit that ends in "do not automate this yet" is a successful audit. We would rather lose the engagement than deploy an operator onto a workflow that is not ready for one.
The task has no judgement in it at all
If a queue is genuinely mechanical, always the same shape, and never needs a decision, a scripted automation is cheaper and simpler. We will say so.
The volume does not justify the engagement
A queue that runs four cases a week does not repay a decision audit. Fix it with a checklist and revisit when the volume grows.
Nobody can say how the decision is made
If the process owner, the team and the manual all disagree and nobody will arbitrate, we cannot codify it. That is an organisational problem first.
The rules are about to change completely
If a regulation or a system migration is going to rewrite the workflow in three months, wait. Tuning on rules that are about to be replaced wastes your money.
These are not mutually exclusive.
Several clients run AutoServa alongside existing automation rather than replacing it, and that is usually the right shape.
Keep the RPA that works
If a scripted bot is reliably handling stable, judgement-free data entry, leave it alone. AutoServa takes the exception queue it keeps failing on, which is usually where the human time was going anyway.
Keep the assistant your staff like
General assistants are genuinely useful for drafting and research. They are a different tool from an operator that works a governed queue, and the two do not conflict.
Build in-house where the logic is your moat
If a process encodes something genuinely proprietary that you would never hand to a vendor, build it. Use AutoServa on the surrounding queues that are eating your team's week.
Test us against your shortlist.
Bring one real queue and the questions from the table above. We will tell you honestly which of the four options we think fits it, and if the answer is not us, you have lost an hour and gained a written decision framework.