We design, build and operate LLM applications, retrieval systems, autonomous agents, computer vision and predictive models — engineered with evaluation, guardrails and observability so they hold up against real users, real data and real cost.
Most AI demos never reach production. We build the part that's actually hard — making intelligence reliable, observable and affordable inside a product people depend on.
Our AI practice spans the full lifecycle: framing the problem in business terms, selecting between foundation models and custom training, grounding models in your data, and wrapping them in the evaluation, guardrails and MLOps needed to operate safely at scale.
We've proven this on our own products — applied AI runs at the core of ReplyFlow, OwnBooks and Healodex — so the patterns we bring to your build are ones we run and maintain ourselves.
Models that sound confident while being wrong are a liability in any serious product.
We solve it with retrieval grounding, schema-constrained outputs, citations, and automated eval suites with human review on high-risk paths.A notebook that impresses in a meeting rarely handles real traffic, edge cases or latency budgets.
We solve it with production architecture: caching, fallbacks, rate limiting, streaming, and load-tested inference behind real SLAs.Unbounded prompts and naive calls turn a promising feature into an unsustainable bill.
We solve it with model routing, prompt and context optimization, caching, batching and right-sized models per task.Without evaluation, you can't tell whether a prompt change or model upgrade helped or quietly regressed.
We solve it with offline + online eval, golden datasets, regression gates in CI, and dashboards for quality, latency and cost.Sending sensitive data to third-party models without controls is a non-starter in regulated domains.
We solve it with data minimization, private/on-region deployment, PII handling, audit logging and human-in-the-loop gates.Standalone AI tools that don't connect to your data or workflow create more friction than value.
We solve it with AI delivered as first-class services inside your product, wired to your data, auth and existing UX.Assistants, chat, summarization, classification and extraction built on GPT, Claude, Gemini and open models, with prompt engineering and structured outputs.
Vector and hybrid search over your documents and data, with chunking, embeddings, reranking and citation so answers are grounded and traceable.
Multi-step agents that call tools and APIs, with planning, memory, guardrails and human-in-the-loop checkpoints for high-stakes actions.
Detection, classification, OCR and document understanding for images, scans and video — on cloud or at the edge.
Classical and deep models for forecasting, scoring, recommendation and anomaly detection, validated against real baselines.
Eval harnesses, safety filters, prompt/version management, model monitoring, drift detection and CI gates — the operational backbone.
We pick the right model and tool per task — and avoid lock-in where it matters.
Foundation models and proven patterns get a credible AI capability in front of users in weeks, not quarters.
Grounding, guardrails and evaluation mean outputs you can stand behind — with audit trails for regulated use.
Model routing, caching and right-sizing keep spend and response times inside the budget you set.
A model-agnostic architecture lets you swap or mix providers as the landscape — and pricing — shifts.
Representative of outcomes from applied-AI systems engineered with grounding, evaluation and cost control.
An AI reply system that drafts, routes and resolves routine conversations, escalating edge cases to humans — the pattern behind ReplyFlow.
ML-driven bookkeeping that classifies and reconciles transactions with audit-grade traceability — applied in OwnBooks.
Decision support that surfaces relevant signals for doctor-in-the-loop review under healthcare constraints — applied in Healodex.
We ground models in your data with retrieval-augmented generation, constrain outputs with schemas and tool contracts, add validation and citation layers, and run automated evaluation suites with human review on high-risk paths before and after release.
No. Many high-value systems run on foundation models with RAG over your existing documents and structured data. Where custom models are warranted, we help with data collection, labeling strategy and fine-tuning, starting from what you already have.
Yes. We design AI features as services and APIs that fit your current architecture, with observability, cost controls and fallback behavior, so they ship inside the product your users already use.
We minimize and isolate sensitive data, support private and on-region model deployment, log and audit model decisions, and design human-in-the-loop controls for regulated or high-impact use cases.
Bring us the problem. We'll tell you honestly whether AI is the answer — and if it is, engineer it to run reliably in production.
Inteople