New to AI? Start with the Starter Pack — website, Google & automation for Vancouver small businesses.

AI implementation & managed operations

Turn your business bottlenecks into working AI systems.

We identify the workflows where AI can create measurable value, prove the opportunity with a controlled pilot, deploy the system into your operations, and keep it performing after launch.

From first opportunity to production and continuous improvement

Your operations, running Diagnosed · Piloted · Deployed · Operated

New to AI? Start simple

The Starter Pack — for Vancouver small businesses.

A warm, inviting independent Vancouver storefront at golden hour.

If "AI" feels like a lot, start here. It's one simple, done-for-you package — from $1,000 — that covers the foundations, plus your first taste of what AI can do.

  • A website that works
  • Get found on Google
  • Email & lead automation
  • Your first AI automation
  • See what's working

Where AI is going

AI is transforming the way businesses operate.

But too many implementations still fail to drive growth. The difference is rarely the model — it is whether the system ever reaches the work.

The gap between a demo and a working system Integration · Permissions · Testing · Training · Ownership

Answers in minutes, not days

Enquiries, tickets and documents are handled the moment they arrive, instead of waiting for someone to notice them.

Capacity without headcount

The repetitive half of a role is absorbed by the system, so the team spends its hours on the work that needs judgement.

Fewer errors on routine work

Rules-based tasks run the same way every time, so rework and the cost of catching mistakes late both fall.

Decisions on current information

Reporting reflects the business as it is today, not as it was when someone last had time to update the spreadsheet.

Coverage that does not sleep

First response, triage and routing continue outside office hours, across time zones, and through busy periods.

Consistent quality

Output no longer varies with who happened to pick up the task, or how much of the day was left when they did.

Where the value is won or lost

What makes the difference

One priority, chosen on evidence We identify which opportunities are valuable, feasible, measurable, and worth implementing — before anything is built.
Demonstrations that reach production We add the integrations, controls, testing, ownership, and operating processes required for daily use.
Built around the workflow We redesign the workflow around the business objective instead of forcing another disconnected application onto the team.
Owned and improved after launch We monitor performance, resolve failures, manage costs, and continuously improve the system.

Models and platforms we build with

Claude ChatGPT Gemini Grok DeepSeek Perplexity Cursor Kimi K3 Qwen 3.6 NotebookLM Manus Lovable Higgsfield

We are vendor-neutral: the model is chosen per workflow on accuracy, cost and data handling. All names and trademarks are the property of their respective owners.

What we improve

AI opportunities exist across your business.

We organise opportunities by business function rather than by industry, because the workflows that waste time look remarkably similar whether you run a clinic, a distributor, or a professional services firm.

A salesperson at a sunlit desk with a laptop showing an AI assistant suggesting follow-ups.
Sales & customer service
Two colleagues watching a screen where an automated workflow routes tasks through connected steps.
Operations & finance
Two colleagues talking across a table with a laptop showing an onboarding checklist completing itself.
HR, management & knowledge

Lead intake

Every enquiry captured, structured, and logged the moment it arrives — regardless of which channel it came through.

Lead qualification

Enquiries scored against your criteria so the sales team spends its hours on the ones worth pursuing.

Personalized follow-up

Follow-up drafted from real account context and sent on schedule, so nothing goes cold because someone was busy.

CRM updates

Records updated from calls, email, and meetings rather than from memory at the end of the week.

Proposal preparation

First-draft proposals assembled from your pricing, past documents, and the specifics of the opportunity.

Sales research

Account and contact briefing prepared before the call instead of during it.

Meeting summaries

Decisions, objections, and commitments captured and routed to the right record and the right person.

Pipeline reporting

Pipeline movement and risk surfaced on a schedule, without a manager rebuilding the spreadsheet.

Customer reactivation

Dormant accounts identified and re-approached with a reason that is specific to them.

All sales workflows & FAQ

How it works

From business problem to dependable system.

Five stages, each with a defined output. You can stop after any one of them, and you own everything produced along the way.

Two people mapping a process on a wall of warm-toned sticky notes.
Diagnose

Free AI Opportunity Scan

We identify where your business is losing time, revenue, capacity, or visibility and determine whether there is a worthwhile AI opportunity.

Coloured cards laid out in an ordered sequence on a warm desk as a hand places one.
Prioritize

AI Opportunity Roadmap

We map the workflow, calculate the baseline, evaluate data and systems, prioritize opportunities, assess risk, and create the implementation plan.

A person at a sunlit desk watching a single rising line chart on a laptop.
Prove

AI Value Sprint

We build one controlled, fixed-scope pilot and test it against an agreed business objective.

A developer at a warm wooden desk with two screens showing connected-system diagrams.
Deploy

Production Implementation

We connect the system to your software, establish permissions and controls, test it under real conditions, train your team, and launch it into daily operations.

A calm sunlit desk with a monitor showing a simple dashboard of gentle line graphs.
Operate

Managed AI Operations

We monitor quality, reliability, usage, cost, integrations, and business performance while continuously improving the system.

Present at every stage, not bolted on at the end Security Data Human oversight Evaluation Cost control
Two colleagues at a bright white table reviewing work together on a laptop.
Stage 01 · Free

Ready for more? Start with a free Opportunity Scan.

Thirty minutes, no pitch deck. You describe how the work moves through your business today; by the end you have one to three opportunities ranked, and a view on which one is worth doing first — whether or not you go further with us.

Evidence

Production results — not AI theatre.

We publish case studies only when a system is running in a client's operations and the client has approved the numbers. Everything below is labelled for exactly what it is.

A support specialist wearing a headset at a bright desk, viewed over the shoulder.
Demonstration Workflow

Inbound enquiry triage

A working demonstration that classifies inbound enquiries, drafts a first response for human approval, and writes the outcome back to a CRM record.

  • Built to show the pattern, not to report a client result
  • No client data involved
A hand sorting neat stacks of paperwork and invoices on a bright white desk.
Concept System

Document intake & coding

A concept system that reads incoming documents, extracts the fields a finance team needs, and queues them for approval rather than posting them automatically.

  • Architecture and controls defined; not yet deployed
Neatly organised labelled binders on white shelving with one folder pulled out.
Internal Test

Knowledge retrieval assistant

An internal test that answers procedure questions from a document set and shows the source passage alongside every answer.

  • Run against our own documentation

Our disclosure rule. We do not present demonstrations as client projects, and we do not publish client names, logos, statistics, or testimonials without written approval. When a case study appears here it will carry a baseline, a measured result, a timeline, and a payback period.

What a published case study will contain

  • Business context
  • Business problem
  • Previous workflow
  • Current baseline
  • System implemented
  • Software connected
  • Human oversight
  • Implementation timeline
  • Performance result
  • Employee adoption
  • Payback period
  • Next opportunity

Engagement model

Start with one valuable problem.

Each stage is a decision point. You can stop, take the work in-house, or continue — and the scope of the next stage is quoted only once we both understand the one before it.

Stage 01

AI Opportunity Scan

Free initial qualification and opportunity identification.

No cost · Apply below

Stage 02

AI Opportunity Roadmap

Paid workflow analysis, prioritization, business case, architecture, and implementation plan.

Scoped after the Scan · May be credited toward implementation

Stage 03

AI Value Sprint

Fixed-scope pilot built around one measurable business objective.

Fixed price · Scoped in the Roadmap

Stage 04

Production Implementation

Complete integration, testing, controls, training, documentation, and launch.

Quoted to the confirmed scope

Stage 05

Managed AI Operations

Ongoing monitoring, support, reporting, improvement, and expansion.

Monthly · Sized to systems under management

Colleagues in a relaxed planning conversation around a bright white table with notebooks and a laptop.

Where it starts

One problem, one conversation

You do not have to know which AI you want, or have a budget approved, or have your process documented. You need one workflow that visibly costs you something, and someone internally who wants it fixed. Everything else is what the first two stages are for.

We do not publish fixed package prices, because two businesses with the same request rarely have the same systems, data quality, or volume. Pricing is quoted against a scope you have seen and agreed.

Frequently asked questions

Straight answers about putting AI to work.

The questions we are actually asked, answered without hedging. See all questions →

What does an AI implementation company actually do?

An AI implementation company takes a business problem from opportunity identification through to a system running in daily operations. That means selecting the workflow worth changing, choosing between buying, configuring, integrating or building, connecting the system to existing software, establishing permissions and human approval steps, testing under real conditions, training staff, and then monitoring and improving the system after launch. It is distinct from strategy-only consulting, which stops at the recommendation.

How do you decide which AI opportunity to build first?

Opportunities are scored against eight criteria: business value, feasibility, time to value, data availability, employee adoption, operational risk, measurability, and implementation cost. The strongest first projects are usually repetitive, frequent, measurable, supported by information the business can already access, expensive enough to matter, easy for employees to review, and limited enough in scope to test safely.

Do you always build custom AI software?

No. There are four valid answers: buy an existing product when it already solves the problem, configure an existing platform when the workflow needs limited customization, integrate existing systems when the real problem is fragmented information and manual handoffs, or build a custom system when the process is unique, strategically important, or valuable enough to justify development. The goal is the most practical solution, not the most complicated one.

What is an AI Opportunity Scan?

The AI Opportunity Scan is a free, focused initial assessment. It includes a short company questionnaire, a review of the largest operational bottlenecks, identification of one to three potential opportunities, a recommended first project, preliminary feasibility, a preliminary value estimate, and recommended next steps. It identifies potential projects. It is not a free custom build, technical architecture, security audit, or complete implementation plan.

Why do most AI pilots fail to reach production?

A demonstration proves a model can produce an output. Production requires integrations, authentication, employee permissions, human approval stages, error handling, monitoring, usage and cost controls, training, documentation, and a named owner. Pilots stall when none of that operating layer is built, when the tool does not fit the actual workflow, or when nobody owns the system after launch.

What are managed AI operations?

Managed AI operations is ongoing ownership of a deployed AI system: monitoring output quality, investigating errors, improving workflows, prompts and models, monitoring integrations, usage and cost, reviewing access and permissions, supporting employees, reporting performance monthly, updating documentation, and reviewing new opportunities quarterly. Models, software, data, and the business all change, so the system needs an owner to stay reliable.

Which business functions can AI realistically improve?

Opportunities are usually found by function rather than by industry: sales (lead intake, qualification, follow-up, CRM updates, proposals), customer service (inquiry classification, routing, knowledge retrieval, escalation), operations (document processing, task routing, scheduling, approvals, recurring reporting), marketing, finance and administration (invoice processing, contract summaries, reconciliation), human resources, management reporting, and internal knowledge retrieval.

Who is a good fit for AI implementation?

Businesses with repetitive or high-volume workflows, disconnected systems, manual administrative processes, slow response or follow-up, limited management visibility, or processes that cannot scale efficiently. Two things matter more than industry: a measurable business problem, and an internal decision-maker committed to solving it.

Get started

Find the first AI system worth building.

Show us where your business is losing time, revenue, capacity, or visibility. We will help determine whether AI can solve it — and what the first practical step should be.

hello@askgeeks.ai · Vancouver, BC · remote across Canada & the US