issues resolved per hour
Average productivity gain in a study of 5,172 customer-support agents using AI assistance. Gains varied across workers.
Quarterly Journal of Economics · 2025
Data & AI engineering. Built around you.
Craft your ultimate
data & AI build.
We engineer trusted data foundations, clear analytics, and practical AI—built to move your business forward.
Microsoft AI Cloud Partner Program member · Based in Canada
The technology behind your build.
Cloud foundations. Connected data. Practical AI.
Technology trademarks belong to their respective owners.
What we build
Purposeful engineering. Practical builds.
Explore data platforms, analytics, and AI builds shaped around real business challenges.
Retail & commerce
Know which price changes need a closer look.
Spend time on decisions, not browsing.
Today
Check retailer pages one by one
With GuildBuild
Review one prioritized watchlist
AI & automation
Let AI prepare the work. Keep people in control.
Move routine requests forward faster.
Today
Read, copy, and draft every request
With GuildBuild
Review a prepared next step
Connected data
Stop copying the same data between systems.
Get consistent numbers with less effort.
Today
Export, paste, and reconcile
With GuildBuild
Receive a checked, scheduled update
Business operations
See who owns the next step.
Keep handoffs moving and follow-ups visible.
Today
Chase updates across inboxes
With GuildBuild
Open one shared view of the work
Microsoft Fabric
Find what is slowing your reports down.
Make your next capacity decision with evidence.
Today
Guess whether to buy more capacity
With GuildBuild
See the workload and test a fix
Analytics & reporting
Turn scattered numbers into a clear next move.
Know what changed and where to focus.
Today
Compare conflicting spreadsheets
With GuildBuild
Discuss one view with source dates
Reference approaches informed by our internal engineering. Scenarios are illustrative, not client case studies or promises of results.
Explore all solutionsMake the business case
Better data. Better decisions. Measurable value.
From a dependable data foundation to smarter use of AI and cloud spend. Explore what changes, what you gain, and how to measure it.
Data engineering & warehousing
Data that arrives reliably. Answers that arrive faster.
Grow your data estate without growing a maze of fragile pipelines. Catch problems early and give every report a stronger foundation.
- Connect sources
- Check & organize
- Serve trusted data
Fewer broken updates
Spot missing, inconsistent, or unusual data before it reaches a decision.
Faster queries, leaner storage
Make busy dashboards more responsive as your data grows.
Room to scale
Bring new sources onboard through repeatable rules instead of one-off rebuilds.
Illustrative planning example
Elapsed time for the same scheduled data load.
minLower is better
How we would validate itCompare run histories at equivalent volumes; check that quality checks and recovery still pass.
Improvement = change in the preferred direction ÷ baseline × 100.
Explore the technical approach
Fewer broken updates
Metadata-driven pipelines make sources, rules, run history, and changes traceable. Error handling, safe retries, discrepancy checks, and data profiling flag missing values, unusual row counts, and outliers.
Faster queries, leaner storage
Review query execution plans, schemas, data types, compression, partitioning or clustering, and maintenance. Apply indexes only where the engine supports them; Fabric Warehouse and Lakehouse need different tuning strategies.
Room to scale
Reusable ingestion patterns, versioned metadata, and documented engineering standards. AI agents use curated skills to check query and pipeline practices, with executable checks and human review before changes ship.
Platform referencesFabric warehouse performance guidance
Business intelligence & measurement
One set of numbers. A clearer next move.
Replace conflicting spreadsheets with consistent reports, agreed KPIs, and fresher information people can act on.
- Agree the measures
- Refresh one model
- Decide with confidence
Consistent reporting
Give every team a familiar view, with less room for misinterpretation.
One definition of success
Discuss what the numbers mean instead of whose spreadsheet is right.
Fresher decisions
See changes sooner, with an explicit date on the data.
Illustrative planning example
Elapsed time from a source change to its availability in the report.
hoursLower is better
How we would validate itCompare source and report timestamps across a representative period, including failures and late arrivals.
Improvement = change in the preferred direction ÷ baseline × 100.
Explore the technical approach
Consistent reporting
Reusable report templates, shared visual standards, accessible layouts, and common filters make reporting easier to navigate and maintain.
One definition of success
Shared semantic models provide a governed source of truth. Document KPI formulas, owners, targets, grain, and source lineage; reconcile measures before publication.
Fresher decisions
Tune model size, refresh schedules, and incremental refresh where supported. Track refresh failures and source-to-report delay against agreed service levels.
Platform referencesPower BI semantic model guidance
Data governance, administration & change
Know what you have. Change it with confidence.
Make data easier to find, understand, and maintain. Give every change a clear owner, a test, and a route back.
- Catalog & explain
- Test & approve
- Release & trace
Find and trust your data
See where data came from, who owns it, and how it should be used.
Repeatable, safer releases
Reduce deployment surprises and make changes easier to audit.
Shared business meaning
Give teams and AI a common understanding of your business concepts.
Illustrative planning example
Share of in-scope data assets with an owner, description, and recorded lineage.
%Higher is better
How we would validate itAudit a fixed asset inventory against the same completeness criteria. Coverage alone is not a compliance certification.
Improvement = difference between the two percentages in the preferred direction, in percentage points (pp).
Explore the technical approach
Find and trust your data
Data catalogs, lineage, ownership, and practical documentation, using Microsoft Purview where appropriate. Review access, classification, and stewardship against your operating requirements.
Repeatable, safer releases
Continuous integration, delivery, and testing (CI/CD/CT), version control, environment promotion, approval gates, and tested rollback. Link changes to their checks and release records.
Shared business meaning
Define terms, entities, relationships, and ownership. Assess Fabric IQ ontology (preview) for suitable pilot use; preview availability and limitations must be checked before adoption.
MLOps, AI enablement & agent engineering
Move from an AI experiment to a dependable capability.
Help people ask better questions, test predictions, and use AI against trusted data—with quality, access, and cost under control.
- Prepare & evaluate
- Deploy with controls
- Monitor & improve
Models you can operate
Know when a prediction is useful and when a model needs attention.
AI connected to your business
Help teams explore approved data in everyday language.
Agents with a clear job
Make AI work repeatable, reviewable, and accountable.
Illustrative planning example
Share of representative test cases that meet the agreed quality and safety criteria.
%Higher is better
How we would validate itUse the same held-out test set, review rubric, and access conditions. Check important failure cases separately from the average.
Improvement = difference between the two percentages in the preferred direction, in percentage points (pp).
Explore the technical approach
Models you can operate
MLOps for reproducible training, versioned data and models, evaluation, deployment, drift monitoring, and rollback. Agree task-specific quality thresholds before release.
AI connected to your business
Enable suitable use cases with Microsoft Foundry (formerly Azure AI Foundry), Fabric Copilot, and Fabric data agents. Validate tenant, capacity, region, and access requirements; evaluate answer quality against known examples.
Agents with a clear job
Curated agent skills, approved tools, evaluation sets, and human approval for consequential actions. Measure accepted outcomes, unsupported answers, latency, and cost—not just how many responses an agent produces.
Also explore automation: team capacity and published research
Published research benchmarks, not GuildBuild client results. Outcomes vary by task, team, and implementation.
less email work per week
Among active users in the latter half of a six-month experiment across 66 firms and 7,137 workers. This did not establish a broader change in task output.
NBER working paper · revised Nov 2025Your opportunity, in numbers
Give your team some time back.
Adjust this planning example to match a recurring task. Include the time people will still spend checking the result.
people × hours/week × target reduction × 48 weeks
Validate this opportunity with usIllustrative estimate, not a forecast or a delivered result. Assumes 48 working weeks and a net reduction after human review. Recovered time is capacity, not cash savings; delivery costs, adoption, and ongoing support are excluded.
Azure & Microsoft Fabric FinOps
Get more useful work from your cloud budget.
Understand what drives the bill. Test workload improvements before buying more capacity, and protect the performance your users need.
- Explain the spend
- Tune the workload
- Verify cost & service
A bill you can explain
Connect Azure and Fabric spend to the teams and workloads creating it.
Capacity backed by evidence
See where contention starts and whether tuning can relieve it.
Less wasted compute
Improve the work each unit of capacity delivers.
Illustrative planning example
Comparable recurring Azure and Fabric charges for the same service scope.
CAD / monthLower is better
How we would validate itUse billing exports with equivalent usage, service levels, commitments, and currency. Net savings must also account for implementation and ongoing costs.
Improvement = change in the preferred direction ÷ baseline × 100.
Explore the technical approach
A bill you can explain
Cost analysis, allocation, budgets, and workload-level unit economics. Separate recurring spend, one-off changes, and commitments before comparing periods.
Capacity backed by evidence
Fabric Capacity Metrics analysis of consumption, peak load, throttling, and workload overlap. Compare scheduling and right-sizing options with the required service levels.
Less wasted compute
Test Lakehouse file layout and maintenance, Spark pool sizing and scaling, job configuration, and query changes. Lower compute use on a fixed capacity may create headroom without reducing the bill.
Illustrative targets, not GuildBuild client results or promised gains. Replace the example inputs with your baseline and a target to test. Compare equivalent workloads, data volumes, and measurement periods; include delivery and ongoing costs in any investment decision.
Build your business case with usFind your next build
Bring the challenge. Explore the build.
Choose what you want to improve. See how data and AI engineering could turn it into a useful build.
Connected data
Your systems, finally working together.
Make databases, applications, and reporting work together without asking your team to keep moving the same data.
Your potential build path
Map
Agree on source owners, field meanings, access permissions, and the data that is actually needed.
Connect
Build and validate transfers with transactions, duplicate handling, and recoverable failures.
Maintain
Monitor freshness and quality, document interfaces, and hand over tested operating procedures.
The craft behind the build
Engineer with purpose. Build for what’s next.
A clear problem. A tested build. The knowledge and ownership to keep moving forward.
Discover & design
We listen to your team, trace the friction, and agree on what progress should look like.
Engineer & build
We build a focused end-to-end solution, test it with real requirements, and show our work as we go.
Launch & evolve
You get the documentation, training, and operating knowledge to move forward with confidence.
The GuildBuild Data Lab
Explore the engineering in action.
Explore public-data reference experiences for Canadian retail and Ontario electricity. See the source, the observation period, and the limits beside the numbers.
Explore the Data LabPublic reference experiences. Availability and freshness are shown in each report.
Why GuildBuild
Serious engineering. A human way of building.
Founder-led, Canadian based
Work directly with the people designing and building your solution. Based in Toronto, serving teams across Canada.
Microsoft expertise
Microsoft AI Cloud Partner Program member, with consulting services listed on Microsoft Marketplace.
Clear ownership
Your systems and knowledge should stay useful after handoff. We design for maintainability, clear documentation, and team ownership.
Ideas you can use
Field notes for your next build.
AI agents
What a cheaper model loses when it summarizes your agent's memory
A summary can sound right and still omit the fact another agent needs. Two memory files reveal why coverage, faithfulness and source checks are separate questions.
Read the insightAI agents
What your agent still knows after a compaction
A file on disk, a message in a log and information the agent can use are different things. Here is what reloads, what needs retrieval and what to preserve.
Read the insightClaude Code
What an effort level buys, and where it stops paying
A 20-question lookup test compares reasoning effort, thinking tokens and estimated cost across three models. High was the stopping point in this test; harder judgment tasks need their own evidence.
Read the insightGood questions
Let’s make the first step simpler.
What does GuildBuild help businesses do?
GuildBuild connects data and business systems, builds Power BI and Microsoft Fabric analytics, and develops practical AI workflows. We start with a business problem and define a focused, testable first step.
Do we need Microsoft Fabric to work with you?
No. We also work with existing databases, APIs, and business applications. We assess your current systems before recommending a platform or migration.
What is harness engineering in plain language?
Harness engineering is the work around an AI model that makes it useful in a real workflow: clear instructions, approved tools, reusable skills, tests, memory, and human approval for consequential actions.
Are the solution stories client case studies?
No. They are reference approaches informed by internal engineering, with illustrative scenarios. They do not disclose private projects, identify clients, or promise the same result for every business.
Where does GuildBuild work?
GuildBuild is based in Toronto, Ontario, and serves organizations across Canada. Delivery arrangements and any data residency requirements are agreed for each engagement.
How do we get started?
Send us the problem you are trying to solve through our contact page. If you would like help organizing the idea first, our Architect tool can help draft a project brief for discussion.
Your next build starts here
Let’s craft your next data & AI build.
Tell us what’s slowing your team down, or what you’re ready to build.
contact@guildbuild.ca