SmartPath AI
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SmartPath AI
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Built for Your Problem. Not Someone Else's.
When your business problem doesn't fit a generic AI tool, we build the solution from the ground up — custom models trained on your data, integrated into your systems, with production infrastructure that keeps them accurate over time.
Data ingestion
12,400 docs · validated
Preprocessing
Chunking · embedding
Fine-tuning
Epoch 1/24
Eval checkpoint
Acc 72.0%
Guardrail tests
248 cases · passing
Staging deploy
Integration checks
Production
MLOps monitoring live
Training loss
MLOps: no drift detected · last retrain 3 days ago · next scheduled in 4 days
14d
Days from scoping to first production deployment
100%
Of engagements start with a defined success metric
3.2×
Average ROI on custom AI vs. off-the-shelf tools
Zero
Production deployments without MLOps infrastructure
Select a domain and run the simulation — see exactly how we assemble training data, fine-tune the model, run evaluation, and ship to production step by step.
PROBLEM: Insurance firm: extract 60+ fields from unstructured submission documents
Training corpus assembled
Document model fine-tuned
Evaluation suite run
Confidence guardrails set
Deployed + MLOps live
From problem scoping to production deployment and beyond — we handle every phase of the AI development lifecycle.
Before writing a line of code, we work with your team to define the exact business problem, the outcome metric that defines success, the data available, and the feasibility of an AI solution — so every engagement starts with clarity, not assumption.
When off-the-shelf AI models don't fit your domain, we build from the ground up — training models on your proprietary data, fine-tuning foundation models on your specific use case, or engineering hybrid approaches that combine multiple techniques.
We don't deliver models in isolation. Every AI system we build is integrated into your existing workflows, systems, and interfaces — so the intelligence reaches the people and processes that need it, not just a data science sandbox.
A model that can't be reliably deployed, monitored, and updated in production isn't a solution — it's a prototype. We build the MLOps infrastructure required to keep your AI system accurate, available, and maintainable over time.
Enterprise AI requires structured evaluation — not just accuracy metrics, but systematic testing for edge cases, failure modes, and unintended outputs. We build evaluation frameworks and guardrails before any model reaches production.
AI systems degrade as the world changes. We provide ongoing monitoring, performance review, retraining on new data, and capability expansion — treating your AI system as a living product, not a one-time delivery.
Three custom AI builds — the specific business problem, the system we built, and what it delivered in production.
The Challenge
A commercial insurance firm was manually reviewing 1,200+ submission documents monthly — loss runs, financials, engineering reports — to extract key risk data for underwriting decisions. Each submission took 3–5 hours of analyst time, creating a 10-day processing backlog and limiting the underwriting team's capacity to grow the book.
What We Built
We built a custom document intelligence platform trained on the firm's proprietary submission library — extracting 60+ structured data fields from unstructured documents, classifying submission risk tier, flagging data quality issues for analyst review, and pre-populating the underwriting system with extracted data. Built on a fine-tuned document understanding model with a human-review interface for low-confidence extractions.
3-5hr→40m
Per-submission processing time
10d→2d
Average submission-to-decision cycle
94%
Field extraction accuracy on held-out set
$1.1M
Annual analyst time reclaimed
"The platform processes what used to take a full analyst day in under an hour. Our underwriters review outputs instead of producing them."
— Head of Underwriting Operations
The Challenge
A corporate law firm's associates were spending 30–40% of their time on first-pass contract review — reading through agreements to identify non-standard clauses, missing provisions, and deviations from the firm's preferred position templates. The work was repetitive, high-volume, and a significant contributor to associate burnout.
What We Built
We built a custom contract analysis engine fine-tuned on the firm's preferred position playbooks and annotated contract library. The engine reads uploaded contracts, identifies clause-by-clause deviations from preferred positions, scores overall risk, and produces a structured redline summary report — giving associates a starting point rather than a blank page.
40%→8%
Associate time on first-pass contract review
2.8×
More contracts reviewable per associate
96%
Clause identification accuracy vs. senior review
4.8★
Associate satisfaction with the tool
"Associates used to dread contract review. Now they use the engine's output as a starting point and focus on the judgment calls that actually need a lawyer."
— Managing Partner, Corporate Practice
The Challenge
A regional delivery network with 180 vehicles and 4,000+ daily stops was running route planning on a legacy optimisation tool that hadn't been updated in 8 years. The tool couldn't incorporate real-time traffic, dynamic stop additions, or vehicle-specific constraints — resulting in routes that field teams routinely deviated from and a 17% gap between planned and actual delivery performance.
What We Built
We built a custom route optimisation engine that incorporates real-time traffic feeds, vehicle-specific constraints, dynamic stop additions up to 90 minutes before dispatch, and driver preference data. The engine runs optimisation at scale in under 4 minutes, integrates with the existing dispatch system, and surfaces route quality scores to planners before dispatch confirmation.
17%→4%
Gap between planned and actual performance
23%
Reduction in total route kilometres
4min
Full network re-optimisation time
$890K
Annual fuel and overtime cost reduction
"Our old tool gave us routes that drivers ignored. This one gives routes they actually follow — because they work in the real world, not just on paper."
— Director of Fleet Operations
Generic tools are built for average problems. Custom AI is built for your specific one.
Off-the-shelf AI tools don't fit your specific domain or data
Custom models trained on your proprietary data deliver domain-specific accuracy that generic tools can't match
AI projects deliver prototypes that never reach production
Every engagement is scoped with production deployment as the definition of done — not a demo or a notebook
Models degrade over time with no one accountable for accuracy
MLOps infrastructure with monitoring, drift detection, and retraining keeps your system accurate as data evolves
No clear way to measure whether the AI solution worked
Every engagement starts with a defined success metric and baseline — so ROI is measurable from day one
From discovery to production — a 4-phase process with defined checkpoints at every stage before proceeding to the next.
We define the business problem, assess your data assets, validate AI feasibility, and agree on the success metric before any development begins. No scope ambiguity, no surprise pivots.
We design the AI system architecture, build a working prototype on a representative data sample, and validate core approach accuracy before committing to full development.
We develop the production system — model, infrastructure, integrations, and user interface — with evaluation checkpoints at each milestone before proceeding to the next.
We deploy with full MLOps infrastructure in place — monitoring, alerting, retraining pipelines — and provide ongoing support to expand capability as your needs evolve.
Tell us the business problem and the data you have. We'll assess feasibility and show you what a custom AI solution can deliver — starting with a scoping session, not a sales pitch.