AI that removes a task, not AI that adds a tab.
Assistants grounded in your own documents, extraction that ends manual data entry, and automation for the process somebody currently does by hand every Tuesday afternoon.
- 2 – 4 weeks
- scope to live
- Your data
- never used for training
- Cited answers
- or it says it does not know
Who this is for
You probably need this if…
01
Someone retypes documents all day
A person converts paper or PDFs into database rows. The hour costs you something, and the errors they occasionally make cost considerably more.
02
Your team answers the same question constantly
The answer exists, in a policy document or a manual or a folder somewhere. Finding it takes eleven minutes. Interrupting a colleague takes two.
03
You have been sold AI that did nothing
A generic chatbot that knew nothing about your business, answered confidently and wrongly, and was switched off inside a month. That is common, and it is not your fault.
What this covers
The work, in detail.
7 capabilities
01
Grounded assistants
Retrieval-augmented answers drawn from your own documents, with a citation attached so anyone reading can check where it came from.
02
Document extraction
Invoices, delivery notes, purchase orders and forms turned into database rows, with confidence scores and human review for anything uncertain.
03
Workflow automation
Classification, routing, triage and summarising. The judgement calls that are too fuzzy for rules and too repetitive to be worth a person.
04
Semantic search
Search that understands what someone meant rather than matching keywords, across documents, tickets, products and internal knowledge.
05
Model evaluation
A scored test set before launch and after every prompt change, so that “it feels better” becomes a number somebody can argue with.
06
Guardrails & fallbacks
Refusal behaviour, escalation paths, and what the user sees when the provider has an outage at ten o'clock on a Monday morning.
07
Recommendations & forecasting
Reorder points, demand signals and next-best-action, built on your own history rather than a general model's idea of your industry.
How we approach it
Positions we actually hold.
Opinions cost something to have. These are the ones we would argue for on your project, including where they make the work slower.
01
Grounded or it does not ship
Answers come from your documents with a citation attached. A system that cannot show its source is a system you cannot defend to a customer.
02
Evaluated, not eyeballed
We build a scored test set before launch. Prompt changes are measured against it, so an improvement is something demonstrable rather than something felt.
03
The fallback is part of the feature
Providers have outages and models refuse. What a user sees on a bad day gets designed at the same time as what they see on a good one.
04
Your data stays yours
No-training terms contractually, no retention beyond the request where the provider supports it, and open models on your own infrastructure where the constraint is absolute.
Technology
What we work with.
Defaults, not requirements. If you already run something else and have a team who knows it, we work in yours.
- Models
- Claude, OpenAI, Llama and other open models on your own infrastructure
- Retrieval
- pgvector, hybrid search, reranking
- Orchestration
- Typed tool calling, Python services, background job queues
- Evaluation
- Scored test sets, regression runs per change
Where this shows up in delivery
- Document extraction runs in production on inventory builds, turning supplier paperwork into stock records.
- We run scored evaluation sets against every prompt change we ship, including on shipped client builds.
- Where a client's data cannot leave their infrastructure we deploy open models rather than declining the constraint.
Bought as part of
This practice is never sold on its own. It is quoted inside one of the engagements above, as part of a single number.
Proof
Where we have actually done this.
Projects we designed, shipped and wrote up. Each one names the decision that was genuinely hard to get right.
Questions
What people ask about ai & automation.
- Will our data be used to train a model?
- No. We use providers under contractual no-training terms, and where the constraint is absolute we run open models on infrastructure you control, so nothing leaves your environment at all.
- What stops it making things up?
- Retrieval grounding plus citations. The system answers from retrieved passages and shows you which ones. Where nothing relevant comes back, it says it does not know rather than filling the gap.
- How do you price AI work when token costs vary?
- The build is a fixed price. Running costs are estimated from your real volume during scoping and passed through at cost, so you see the provider's bill rather than our markup on it.
- Can you add AI to software you did not build?
- Usually, yes. What matters is whether the data is reachable through an API or a database. Week one tells you either way, before you have committed to a build.
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Tell us what you’re trying to build.
Describe the problem in your own words. We’ll come back within one business day with a scope, a number and a date.
Phone
+1 (407) 796-2376Reply time
One business day, from an engineer
Based in
Orlando, Florida · serving the United States
What happens next
- A reply within one business day, from an engineer
- A thirty-minute call, with no qualifying call before it
- A written scope and a fixed number, if it fits