AI Integration For The Software
Your Business Already Runs.
Most operations do not need different software. They need one expensive job inside the software they already have — reading the document, drafting the quote, finding the answer — to stop taking an afternoon. We connect an AI model to the systems, records and rate cards you already run on, scope it to one workflow, and put a person in front of anything that carries consequence.
The System Works. The Afternoon Still Goes.
The software is fine. One job inside it is not
The system holds the work correctly. It is the reading, the drafting and the looking-up around it that still costs a person an afternoon — and none of that is a reason to replace software that otherwise does its job.
The answer exists, in a document nobody can find
Specs, contracts, submittals, manuals, past jobs. The answer is in there, and finding it means asking the one person who remembers which folder it lives in.
Everything gets retyped into the next system
A PDF arrives and somebody keys it into the system by hand. The typing is the slow part, and it is also where the errors come from.
AI was tried once and quietly dropped
Someone pasted company information into a public chat tool, got a decent answer, and stopped — because there was no way to do it repeatably, no permissions around it, and no record of what it was told.
Nobody will sign off on output they cannot check
The blocker is rarely capability. It is that a price, a submission or a client-facing document cannot leave the building unless a named person approved it and the system can prove it.
It works in a demo and nowhere else
A prototype answers well on a prepared example, then meets a real inbox, a real scan and a real exception — and there is no defined behaviour for any of them.
One Workflow At A Time.
These are the jobs that come up most often in construction and field operations, energy and industrial services, manufacturing, property management and distribution. Each one is a build in its own right — follow the link for how that piece works.
Reading bid and tender documents
An opportunity lands as a PDF. The model reads it against your service lines, equipment and coverage, and puts the relevant few in front of a person instead of all of them.
Document data extractionDrafting quotes against approved rates
A first draft priced from your own rate card and margin rules — not invented numbers — so the estimator edits and approves rather than starting from a blank page.
Rate cards & contract pricingSearching controlled company documents
Answers drawn from your own specs, contracts and past jobs, with the source document cited, and scoped so people only reach what their role allows.
AI trained on your dataRouting what arrives
Inbound requests, tickets and enquiries read and sorted to the right queue with a reason attached, so the triage is done before anyone opens the inbox.
AI intake & triageSummarising a job's history
The thread, the change orders and the site notes condensed into what somebody needs before a call, assembled from records the system already holds.
Operational softwareChecking a document against a standard
A submittal or certificate compared to the spec it has to satisfy, with the mismatches flagged for a person to judge rather than silently corrected.
Proposal & contract automationThe Task Picks It. Not The Logo.
We build with Claude, Gemini, OpenAI and Grok. We are not a reseller for any of them and hold no partnership or certification with any provider — which is precisely why we can pick on merit for the job in front of us.
Because you own the build, the choice is not permanent. Models improve, prices move and providers deprecate versions; swapping one out later is a change order, not a rebuild.
What the task actually is
Long-document reading, short structured extraction, drafting in your voice and code generation are different jobs, and the models are not equally good at each. The task picks the model, not the other way round.
What it has to connect to
The integration has to reach your systems. Available connectors, API behaviour and how cleanly a model returns structured output often matter more than a benchmark score.
What the data requires
Where the data may be processed, what may leave your environment at all, and what your own client agreements commit you to. Some workflows rule out some options before capability is discussed.
What it costs to run
Per-call cost against volume. A more expensive model is right for a low-volume, high-value document and wrong for something that runs ten thousand times a month.
How much review it needs
A workflow where a person checks every output tolerates a different trade-off than one that runs unattended. We size the review to the consequence of being wrong.
Five Steps, One Workflow.
Map one costly workflow
We pick a single workflow that measurably costs your team time, and write down how it runs today — who touches it, where it waits, and what a correct outcome looks like. One workflow, not a strategy deck.
Assess systems and data access
What the model would need to reach, what can be reached through a supported interface, and what cannot. This is where most of the honest constraints surface — an old system with no API changes the shape of the build.
Build and test the integration
Built against your real inputs, not prepared examples, and tested on the awkward ones: the bad scan, the exception, the case your team argues about. Output is checked against what a person would have produced.
Train the team who use it
The people doing the work learn where it fits, what it does not do, and how to reject an output. An integration nobody trusts gets routed around, which is the same as not shipping it.
Monitor and support it
Defined monitoring, a route for reporting a bad output, and an owner. Models and APIs change under you, so an integration is something that gets maintained rather than delivered and abandoned.
What Happens When It Is Wrong.
A model will eventually return something wrong, and the provider will eventually be unavailable. Both are design questions, answered before launch rather than discovered in production.
Permissions follow your existing roles
The integration reaches what the person using it is already allowed to reach. It does not become a side door to records someone could not otherwise open, and it is scoped to the systems the workflow needs rather than everything you run.
A named person approves what carries consequence
Anything priced, client-facing or contractual stops at a review step before it goes anywhere. The approval is recorded — who approved it, when, and what they were shown.
Low confidence routes to a human, not to a guess
Where a model is uncertain, the work goes to a person with the uncertainty visible, rather than producing a confident answer nobody flagged. Extraction carries a confidence score per field.
Failure is defined before launch
When the provider is down, rate-limits or returns something unusable, the workflow falls back to how it ran before — queued for a person, with the failure logged and visible. It does not fail silently and it does not block the job.
What it was asked and what it answered is logged
Inputs and outputs are recorded, so a disputed result can be traced back rather than argued from memory.
You own the build
The integration, the prompts and the configuration are yours, in your repository and your accounts. Swapping the model later is a change we can make, not a renegotiation.
Deployed, Not Demonstrated.
An Alberta demolition and site-clearing contractor runs a bid intelligence system we built. It monitors provincial and municipal procurement sources, reads each posting against that company's service lines, equipment and crew capacity, prices it from their own equipment rates and target margin, and generates a complete written submission. The operator opens it, edits, signs off, and the system handles submission back to the portal. That approval step is the part worth noting here: the document does not leave without a person.
The reported result is two to three hours per bid down to minutes. That figure belongs to the whole system — monitoring, scoring, pricing and generation together — not to a model in isolation, and it is client-reported rather than independently audited. Our other published results are operational software and quoting outcomes, not AI ones, and we are not going to relabel them.
- We do not train or fine-tune a foundation model on your data. We connect an existing model to your systems and control what it is given.
- We are not a reseller and hold no partnership, certification or exclusive arrangement with any model provider. Model choice is a build decision we make with you and can revisit.
- We do not put AI in front of a decision that needs a licensed professional. Work that requires an engineer, an accountant or counsel gets routed to one.
- We do not replace your systems to add AI to them. If the honest answer is that the underlying record has to be fixed first, we say so on the call.
- We will not ship an integration with no review step on output that carries contractual or financial consequence.
Systems Integration
Connecting your accounting, CRM and field systems to each other so a record entered once is correct everywhere. No model involved.
Applied AI & Automation
Building a system that carries a defined multi-step task from start to finish, rather than adding one capability to software you already run.
AI Trained on Your Data
Answers drawn from your own documents, with the source cited — the retrieval side of this work, covered in depth.
Before You Book The Call.
Do We Have to Replace Our Existing Software?
No, and that is the point of this work. An integration connects a model to the systems, documents and records you already run. Replacing software is a separate and much larger decision — if your current system genuinely cannot support the workflow, we will tell you that rather than building around a problem, but that is the exception, not the default.
Which AI Model Should We Use?
That is a decision we make with you against the task, not a preference we bring. We build with Claude, Gemini, OpenAI and Grok, and the choice comes down to what the job actually is, what the integration has to connect to, what your data requires, what it costs at your volume, and how much human review the workflow needs. We are not a reseller for any provider, and because you own the build, the model can be changed later without starting over.
What Happens to Our Company Data?
Only what a workflow needs is sent, and only to the provider that workflow uses. We scope access to the systems and documents involved rather than opening everything, and we keep a record of what was sent and what came back. The current third-party AI providers we build on are listed on our compliance page, with the region each processes in. Data-residency commitments you have made to your own clients are part of the scoping conversation, not an afterthought.
Does a Person Review the Output?
For anything priced, client-facing or contractual, yes — a named person approves before it leaves, and the approval is recorded. For lower-consequence steps like sorting an inbox or summarising a thread, the work runs and a person can correct it. We size the review to what being wrong would cost, and we will not ship an unreviewed step on output that carries contractual or financial consequence.
How Is This Priced?
Scope drives price and timeline, and both are written into a scope agreement before work starts. The 30-minute call is where a real number gets put on it. Two costs are worth separating: the build, which is ours, and the ongoing per-call cost of the model itself, which is billed by the provider on your own account — we size that against your expected volume during scoping so it is not a surprise later.
What Does Ongoing Support Involve?
Monitoring that the integration is running and producing usable output, a route for your team to report a bad result, and an owner on our side. Model APIs change, providers deprecate versions and your own processes move, so an integration is maintained rather than delivered and left. What that looks like in practice is agreed during scoping rather than assumed.
Bring One Workflow
That Costs You Time.
Not an AI strategy — one job your team does over and over that eats an afternoon. Thirty minutes, no deck. We will map how it runs today, tell you whether a model genuinely helps, and say so plainly if it does not.
Where To Go Next.
Up a level
All Solutions
Everything we build for Alberta operators, in one place.
More In This Series
Customer & Partner Portals
Job status, approvals and documents your customers reach themselves.
AI Trained on Your Own Data
Answers drawn from your documents, not the open internet.
Operational Analytics & Reporting
Custom dashboards built on the data your operation already produces.
AI Consulting & Strategy
Roadmaps, readiness and an honest read on what is worth building.
AI Training & Team Enablement
Getting a team from AI-hesitant to using it on real work.
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