AI implementation
Put AI inside the workflows your company already runs.
Not an AI strategy. Not a proof of concept that never leaves the sandbox. The specific step in your real process where a model earns its place, built in, measured, and operated after launch.
- From $4,500
- About 4 weeks to launch
- 30 days operating support
Where the AI question usually shows up
01
The pilot never became production
A demo that impressed everyone and could not be connected to the systems where the work actually happens.
02
Documents are read by people
Invoices, orders, forms and specs arrive as PDFs and email, and someone keys the contents into a system.
03
Knowledge is spread across four places
Staff search policies, shared drives and databases to answer questions that have a definite answer.
04
Judgement calls at volume
Classifying, prioritising and routing work that is too varied for rules and too repetitive to be a good use of people.
05
Nobody owns accuracy
Something AI-shaped is running and no one is measuring whether it is right, so no one will notice when it stops being right.
What we actually deliver
A measured accuracy baseline
On a real sample of your own data, before anything goes live.
The AI step, built into the process
Extraction, classification, retrieval or drafting, producing structured output that ordinary code can validate.
A human review path
Everything under the threshold reaches a person with the source document and the reason it stopped.
Decision logging
Every AI decision recorded with its inputs and confidence, so a wrong one can be found and the rest checked.
AI at the edges, software in the middle.
The model does the part that resists structure. Everything downstream of it is ordinary, testable code — which is why we can tell you what the system will do.
- Unstructured input, fuzzy classification and retrieval: a good use of AI.
- Arithmetic, lookups, thresholds and validation: not a good use of AI, and cheaper as code.
- A confidence threshold decides what a human sees. It is set from measurement, not from a default.
- We will tell you which parts of the system we propose contain no AI at all, and why.
What fixing the AI question looks like in practice
Document extraction
PDF or email → structured record → validated → system of record
Varied layouts read into consistent fields, with confidence per field and low-confidence ones routed to a person.
Classification and routing
Request → category → priority → the right person
Incoming work sorted the way your best operator would sort it, at a volume they could not sustain.
Internal knowledge assistant
Policies + documents + databases → answer with a source
Staff ask in plain language and get an answer they can check, instead of asking a colleague who is busy.
Exception detection
Transaction stream → anomaly → review queue
The things that do not look right surfaced early, while they are still cheap to fix.
What this looks like in production
Case studies that used this capability
AI · Automation · Integration
Right-hand.ai
Security awareness training
Security training content had to be tailored per employee, and the team could not produce and deliver it at that granularity by hand.
The rest of what we do
- AI implementationYou are here
- Workflow automation
- Integrations
- Internal applications
- Managed systems
Questions about the AI question
How accurate is it, really?
That depends entirely on your data, which is why we measure it on a real sample before committing to anything. What we can promise is that you will see the number before launch, that the confidence threshold will be set from it, and that we keep measuring it in production.
Which model do you use?
Whichever performs best on your task at an acceptable cost, and we tell you which one and what data reaches it. That choice is a maintenance decision as much as a technical one — models change, so the system is built so the model can be swapped and re-evaluated against the same baseline.
Will our data be used to train someone’s model?
No. We use enterprise or API tiers configured so your content is not used for training, and the provider, the data sent and the retention setting are written into the project documentation. Where data cannot leave your environment at all, we say so up front and design around it.
Do we have to give you access to our production systems?
Eventually, in a controlled way, for the systems the workflow touches. Access is named, scoped to what the workflow needs, documented and revocable. We prefer a dedicated service account in your own identity provider so you can turn us off independently of anything else.
What happens if an automation makes a wrong decision at 2am?
The design decides that before launch. Where a wrong call would be expensive, the workflow stops and routes to a person instead of guessing. Failures are logged and alerted, failed items are queued rather than dropped, and there is a rollback path. We operate what we build, so the alert comes to us.
Tell us what you were hoping AI would fix.
We will tell you honestly which parts of it AI is actually good at, which parts are a rules problem wearing an AI costume, and whether the whole thing is worth building.
- For businesses with $5M to $30M in revenue
- No long-term commitment
- Start with one workflow
- Keep ownership of everything we build