AI Integration
Provider-agnostic AI integration for companies that need the systems to work in production — not just demo well. From assessment to deployment to ongoing operations.
Book a Discovery CallAI integration at ScaleLogic is led by engineers who built production systems before large language models existed. The boring parts of a deployment — the database, the queue, the failure modes, the observability, the migration plan — are not afterthoughts. They are what separates an AI demo from an AI system.
The case studies on this site include production AI work shipped under regulatory pressure, multi-provider architectures designed for cost and vendor independence, and machine learning models running across server and browser at compliance scale. The work behind the engagement is the work in the case studies — not a deck.
The kinds of AI work we have shipped to production. Most engagements involve several of these in combination, calibrated to what your business actually needs.
Embed language models into your existing product or internal workflows. Provider-agnostic from the architecture up, so you are not married to a single vendor when prices change or models improve.
Internal copilots, customer-facing assistants, document workflows, and multi-step agents built against your data, your domain, and your tolerance for error.
Extraction, classification, semantic search, and summarization at scale. The unglamorous AI work that quietly removes hours from your operations team every week.
Vector databases, embedding pipelines, retrieval-augmented generation, prompt orchestration, and the observability layer that lets you see what the model is actually doing in production.
Run multiple commercial APIs and self-hosted open-weight models behind a single abstraction. Route by cost, by capability, by latency, or by client preference — without rewriting application code.
For regulated industries or data-sensitive workloads, we host models inside your environment or on our private colocation infrastructure. Your data does not leave your control.
Token economics, model selection, caching, and prompt compression strategies that bring real production AI costs into the range a business case can survive.
When the question is "where do we even start," we map the opportunities, rank them by impact and feasibility, and recommend the one to build first. Paper deliverable, no theater.
A short sample of production AI from real engagements. Each links to the full case study.
Multi-tenant SaaS with a provider-agnostic LLM abstraction across five model providers, bring-your-own-key per tenant, semantic search via pgvector, token analytics, and owned email infrastructure for AI-generated outreach. Architected for cost optimization and vendor independence from day one.
Read the case studyFifteen-plus pre-trained ML models deployed across server (PyTorch) and browser (TensorFlow.js, WebGL, WASM, WebGPU) for face detection, age estimation, anti-spoofing, and liveness. Multi-algorithm consensus across three independent face-matching libraries, calibrated to regulatory thresholds in 25-plus jurisdictions.
Read the case studyMulti-source AI research platform aggregating fragmented financial data, respecting per-API rate limits, and maintaining conversation context across analyst sessions. Real production AI for a real industry workflow — not a generic ChatGPT wrapper.
Read the case studyWhat separates this from a generic AI consultancy.
We have built production systems across multiple commercial AI providers and self-hosted open-weight models — designed from the start so the underlying model can be swapped without rewriting application code. We do not have a favorite vendor. We pick the right model for the job and design the architecture so you can move when something better arrives.
Most firms calling themselves AI consultants started two years ago. ScaleLogic engineers built production systems before LLMs existed — which means the parts of an AI deployment that decide whether it survives (the database, the queue, the failure modes, the observability, the migration plan) are not afterthoughts.
We have built and operated AI systems under PCI-DSS, multi-jurisdiction regulatory frameworks, and biometric privacy requirements. If your AI work has to defend itself to auditors, lawyers, or risk committees, that is familiar territory.
For workloads that cannot run on hyperscalers — sensitive data, regulatory pressure, or simply unsustainable cloud egress costs — we offer model hosting on our private colocation infrastructure. A real alternative to the major clouds, not a slide-deck promise.
The person you talk to during the engagement is the same person who designed the architecture and is responsible for the outcome. No junior implementation team behind the scenes.
Most prospects land here from one of two angles. Both lead to a conversation.
You already know what you want
If you already know what you want to build — a custom assistant, a document pipeline, a model migration, an AI feature in your product — let’s get on a call and scope it directly.
Book a discovery callNot sure where to start
If you haven’t figured out where AI fits in your business, which opportunities are real, or which providers and approaches make sense, we offer paid strategy engagements to work through it. Written deliverable, no implementation commitment.
Talk to us about an assessmentBook a discovery call — no obligation. We will give you an honest read on whether AI integration is the right move for your situation, what shape of engagement makes sense, and whether ScaleLogic is the right partner. If we are not, we will point you toward someone who is.
Book a Discovery Call