Service 03
Practical AI for real business workflows
AI is genuinely good at a narrow set of things: reading unstructured documents, classifying text, summarising, and finding relevant information in a pile of it. Those happen to be tasks that consume a lot of office time. We build AI into workflows where it earns its place, with a human checking the output wherever the cost of being wrong is real.
- The problem
- Documents arrive as PDFs, scans and emails, and somebody has to read every one and type the contents into a system.
- What we do
- We build extraction, classification and knowledge workflows that do the reading, then route anything uncertain to a person for confirmation.
- Business value
- Document handling that scales without more headcount, with a review step so accuracy stays under your control.
Understanding ai automation
AI automation means using language and vision models as one component inside a normal software workflow — not as the whole product. The model does the part traditional code is bad at, such as reading a supplier invoice that has a different layout every time. The surrounding system does everything else: validation, storage, business rules, audit trail.
This distinction matters, because it is what makes AI safe to deploy in operations. A model on its own gives you a plausible answer with no accountability. A model inside a designed workflow gives you an answer, a confidence signal, the source it came from, and a queue where a human confirms anything below the threshold.
We are deliberately conservative about where AI belongs. If a task has fixed rules, ordinary automation is cheaper, faster and more reliable, and we will build that instead.
Signals this applies to you
- Finance and procurement teams keying in supplier documents
- Businesses with a shared inbox that several people manually sort
- Companies whose knowledge lives in hundreds of documents nobody can find
- Support teams answering the same questions from the same policy documents
What Silver Shark can build
Concrete deliverables rather than categories. Most projects combine several of these.
Invoice & document data extraction
Supplier invoices, delivery orders, purchase orders and forms read into structured fields ready for your system, with low-confidence values flagged for review.
Document classification & routing
Incoming files and emails sorted by type, customer or department and sent to the right queue automatically.
Internal knowledge search
A search assistant over your own SOPs, manuals, contracts and past quotations that answers with the source document attached.
Customer support assistants
A first-line assistant grounded in your real product and policy documents, with a clear hand-off to a human when it is out of its depth.
Email classification & triage
Shared inboxes sorted by intent — enquiry, complaint, order, payment advice — so each one reaches the right person without manual sorting.
Summarisation
Long threads, reports and meeting notes reduced to the decisions and actions, with the original always one click away.
Quotation assistance
Draft quotations assembled from a request and your historical pricing, prepared for a person to check and send rather than sent automatically.
Human-in-the-loop review queues
The interface that makes the rest possible: confidence thresholds, side-by-side source and extracted data, one-click correction, and correction history.
How the work runs
Specific to this service — the general delivery sequence is on every project.
- 01
Establish the accuracy bar first
Before building, we agree what accuracy is required and what a mistake costs. That determines whether a workflow can run unattended, needs sampling, or needs every output confirmed.
- 02
Ground the model in your data
For knowledge and support use cases, answers are generated from your own documents and cite them, which limits invention and lets a reader verify the source.
- 03
Design the review step as a real interface
The human check must be faster than doing the task manually or the whole thing fails. Review screens get the same design attention as the rest of the system.
- 04
Measure it against reality
We test against a set of your real documents, not curated samples, and report the actual pass rate before anything goes live.
The sequence every project follows
- 01
Discover
Understand the operation before discussing technology.
- 02
Analyse
Decide what should be removed, automated or built.
- 03
Design
Design the data model and the screens people will live in.
- 04
Build
Build the core in usable stages.
- 05
Integrate
Connect the system to everything else that holds the truth.
- 06
Test
Test with your data and your awkward cases.
- 07
Launch
Go live in a controlled, reversible way.
- 08
Support
Keep it working and keep it improving.
AI Automation — common questions
Is AI accurate enough to trust with our documents?
For extracting fields from business documents, modern models are strong but not perfect, which is why we build a confidence threshold and a review queue. Typically a large share of documents pass straight through and the remainder get a few seconds of human attention — far less work than keying in every document.
Where does our data go?
That is a design decision we make with you. Options range from a commercial model provider under a business agreement with no training on your data, through to models running on infrastructure you control. Sensitive categories can be excluded from AI processing entirely.
Can AI answer customer questions on its own?
It can handle repetitive, document-answerable questions well. We recommend a defined scope, a visible hand-off to a human, and no authority to make commitments about price, delivery or liability.
Do we need AI at all?
Often not. If your inputs are structured and the rules are fixed, conventional automation is cheaper and more predictable. AI is worth it specifically when the input is unstructured — documents, free text, images.
What does it cost to run?
AI workflows carry a per-document or per-request running cost on top of development. We estimate that from your actual volumes before you commit, so the ongoing number is known upfront.
Next step
Tell us where ai automation would help.
Describe the process as it runs today — including the parts that are held together by people. We will tell you what is worth building, what is worth automating, and what is worth leaving alone.