AI Integration
AI Integration Into Products That Already Exist
We do not build AI from scratch and we do not rewrite your product to add it. We find the highest-leverage insertion points in your existing system, add AI where it removes real friction, and ship it with the monitoring and guardrails that keep it from causing new problems.
What We Typically Integrate
The most impactful AI integrations are usually not the flashiest. They target the workflows where your team currently spends the most manual time on repetitive decisions with well-defined inputs and outputs.
Document Review Automation
Contracts, applications, compliance documents, driver licenses — any workflow where humans currently read and classify documents can be restructured around AI pre-screening that surfaces only the genuinely uncertain cases.
Driver and User Verification
AI-assisted triage that scores incoming verification records by risk level, routes low-risk records through automatically, and flags the ambiguous ones for human review. Reduces review team size without removing oversight.
AI-Assisted Operations
Customer support triage, ticket routing, escalation detection, and resolution suggestions. AI handles the predictable tier-1 volume so your team focuses on the cases that actually need judgment.
LLM Features in Workflows
Search, summarization, extraction, classification, and generation — embedded into your existing admin tools, mobile apps, or web products via API calls to foundation models with retrieval augmentation on your data.
Recommendation Systems
Personalized content, product, or service recommendations built on your transaction and behavioral data. Starts simple, improves as you accumulate signal.
Anomaly Detection
Fraud signals, unusual patterns in driver or user behavior, financial anomalies — detected automatically before they escalate, with human review for confirmed cases.
How We Approach It
Most AI projects fail because they start with technology and work backward. We start with the operational problem, find the smallest AI intervention that meaningfully reduces friction, and build outward from there.
Find the insertion point
We map your current workflow to identify where AI can remove the most manual work with the least disruption. This is almost never where people expect — it is usually a step two or three levels upstream from the visible bottleneck.
Retrieval augmentation over your data
Foundation models (GPT-4, Claude, open-source alternatives) combined with your own data via RAG. The model answers questions from your actual documents, records, and history — not from its training knowledge. Reduces hallucinations, improves relevance.
Guardrails and confidence scoring
Every AI output has a confidence score. Below-threshold results are routed to human review instead of being acted on automatically. The system knows what it does not know.
Incremental rollout with monitoring
Feature flags let you test on a percentage of traffic before full rollout. We instrument accuracy, latency, and error rates from the start — so you can see whether the AI is actually improving the workflow or just adding complexity.
Case Study
AI Operations · Mobility
20-person review team reduced to 5 — without removing human control
A mobility platform had 20 people continuously checking incoming driver verification records. We redesigned the workflow with AI-assisted triage: low-risk records were processed automatically, medium-risk records were pre-scored for reviewers, and only the genuinely ambiguous cases required full manual attention.
Related Reading
Frequently Asked Questions
Will AI break what is already working?
Not if it is added correctly. We run AI features behind feature flags, test them on a subset of traffic, and only expand rollout after the results validate. What is already working stays working.
How long does it take to add AI to an existing product?
A working prototype that demonstrates the value can be ready in 2–4 weeks. Hardening it for production — with monitoring, guardrails, fallbacks, and edge-case handling — typically takes another 4–8 weeks. The total depends on how complex your existing system is to integrate with.
Do you build custom models or use existing ones?
Almost always existing models — GPT-4, Claude, Gemini, or open-source alternatives — combined with your own data via retrieval augmentation. Custom model training is expensive and rarely necessary. We start with the simplest approach that solves the actual problem.
How do you handle hallucinations and accuracy?
Retrieval augmentation (RAG) grounds the model in your actual data instead of its training knowledge. Confidence scoring lets the system know when to flag uncertainty. Human-in-the-loop checkpoints handle edge cases that the model should not decide alone.
What if our data is messy or unstructured?
That is the normal situation. We start by auditing what you have, cleaning and chunking it for retrieval, and building a pipeline that ingests new data as it is created. The AI is only as good as what it can retrieve.
Can you add AI to a mobile app?
Yes. AI features in mobile apps typically run via API calls to a backend inference layer — the model does not run on-device unless there is a specific reason for it. We handle the backend integration and surface the result cleanly in the mobile UI.
Know where AI could help but not sure how to add it?
Describe the workflow you want to change. We will tell you what is realistic, what the right approach is, and whether it is worth doing at all.
Start the conversation →