Define a measurable task
Identify what the system must return and which mistakes require human intervention.
Make AI useful inside a real product.
Practical AI workflows, image-based extraction, search and third-party integrations designed for verifiable outcomes.
A convincing AI demonstration is not the same as dependable product functionality. Real users need to know what the model identified, what it could not verify and how to correct a result before it changes important data.
Lortie Labs integrates models and APIs into usable workflows: collecting inputs, checking results, presenting source evidence where available, preserving manual overrides and handling provider errors. For image-based projects, that may include rotation, detection, cropping, metadata extraction and verification before saving.
WHEN THIS FITS
Built for the right kind of project.
Products needing AI-assisted data extraction
Teams connecting systems through APIs and webhooks
Businesses requiring image recognition with human review
Founders planning a subscription product with AI features
THE DELIVERABLES
What the work can include.
The written scope defines exactly which of these items is delivered; extras are priced separately.
Defined input/output contracts and data validation
Provider integration, authentication and access limits
Confidence handling, error states and manual review
Source-traceable or evidence-aware outputs when feasible
Logs, retry behavior and usage-budget planning
Representative tests for incorrect, partial and unavailable results
BUILD PROCESS
A practical development workflow.
Choose data and providers
Evaluate documentation, API access, limitations, data handling and usage costs.
Build verification into UX
Make uncertain results, source comparisons and correction paths visible rather than silently accepting an answer.
Test failure conditions
Check timeouts, invalid files, mismatched identities, rate limits and the cost of operational monitoring.
SCOPE & INVESTMENT
What influences the final price?
Model accuracy expectations and evaluation data quality
Third-party API limits, licensing and costs
Image volume, latency and document variety
Sensitive records, data residency and privacy obligations
Multiple providers, custom datasets and billing complexity
All estimates are in Canadian dollars before applicable taxes. Platform subscriptions, payment processing, shipping, hosting, AI usage and ongoing support are scoped and quoted separately. No guaranteed rankings, provider approvals or regulatory outcomes.
COMMON QUESTIONS
Before you begin.
Can AI always identify the correct image or record?
No. Accuracy varies with inputs, model capabilities and reference data. Verification thresholds and human correction are important parts of the implementation.
Will I need to pay for AI APIs after launch?
Often yes. Model, OCR, image processing, storage and third-party search providers commonly have usage-based charges. Those costs should be estimated independently of the initial development fee.
Can an existing business application gain AI features?
Often. A narrowly scoped integration can be added after identifying the system of record, provider interfaces, permissions and acceptable failure behavior.
REAL EXPERIENCE / PROJECT REFERENCE
Trading-card identification and verification work
RubyCollect includes AI-assisted identification and research features. A separate card-scanning API is being developed; it is not offered as a public production API yet.