AI-native platforms,
built to survive production.
Custom copilots, retrieval-augmented platforms, agentic workflows, and ML systems. Built to run inside your stack, to meet your compliance constraints, and to still be working six months in.
Where AI usually breaks.
An impressive AI demo and a working AI system are two different things. The first one survives a 30-minute walkthrough. The second handles the messy reality of production data, holds up under your compliance constraints, and stays correct as your business changes around it.
The gap between them is engineering work, and it's where most AI initiatives quietly stall. Models that hallucinate when fed real records. Retrieval that works on a curated PDF and falls apart on the actual document corpus. Agents that make sense in isolation and lose the thread three tool calls in. Pipelines that nobody can explain six months later, when a regulator asks how a decision was reached.
Our work is the second kind. AI systems that are observable, evaluable, restrainable, and where the situation calls for it, auditable. They get built the way real software gets built.
Retrieval quality on real-world document sets. The polished sample used in the prototype rarely predicts what happens at scale.
Evaluation harness, guardrails, and observability. The monitoring scaffolding that decides whether the system is trustworthy in production. Most of it never makes it into the demo.
Regulated industries need audit trails and explainability. We build those in from the start of the project. Adding them at audit time is far more expensive.
Five shapes of AI work.
Most engagements land in one of these patterns. Each is its own discipline, with its own evaluation methodology and operational shape.
Custom Copilots
Domain-specific assistants embedded in the workflows your team already uses: clinical, financial, operational. Grounded in your data and governed by your policies, with everything they do landing in your audit trail.
RAG & Knowledge Systems
Retrieval-augmented systems that bring your documents, tickets, and structured data into the model's context. With the evaluation harnesses to know whether retrieval is actually doing the job.
Agentic Workflows
Multi-step, tool-using systems for the work humans shouldn't be doing by hand: researching, drafting, reconciling, routing. With explicit boundaries, fallbacks, and human checkpoints at the steps that actually need them.
ML Systems & Model Development
Classical and deep-learning models for the use cases that genuinely need them: forecasting, classification, anomaly detection, ranking. Backed by the data pipelines, monitoring, and retraining cadence to keep them honest over time.
AI Integration into Existing Systems
The most common engagement: you don't want a new product, you want AI cleanly added into the platforms you already run. We work inside your existing architecture (your auth, your data, your deployment model) and ship AI capabilities that read as native features. This is what most "AI strategy" actually looks like once it meets the constraints of your real environment.
An AI-native system, end to end.
Simplified, but representative of what we deploy on most engagements. Every block is opinionated. These are choices we'd make over the alternatives, and we'd defend them in front of your security team.
Engineering, not wiring.
RAG with restraint
Below is a simplified version of a retrieval flow we’d ship. Typed, observable, and written to fail loudly rather than silently.
Note the confidence threshold, citation requirement, and structured fallback when retrieval quality drops. These are the pieces that make AI systems trustworthy in production.
import { retrieve, rerank, generate, log } from '@apollo/rag' import { z } from 'zod' // Structured output. Model must cite sources. const Answer = z.object({ response: z.string(), citations: z.array( z.string().url()).min(1), confidence: z.number() .min(0) .max(1), }); export async function answer(query: string, user: User ) { const hits = await retrieve({query, user, k: 20}) const ranked = await rerank({query, hits, k: 5}) // If retrieval is weak, fail safely if ( ranked[0].score <0.62) { return {kind: 'no_match' } as const } const result = await generate({ schema: Answer, context: ranked, policy: user.policy, }) log({query, user, hits: ranked, result}) // audit trail return {kind: 'answer', ...result } as const }
Four phases, scoped for AI work.
Apollo's standard methodology, applied to the specific failure modes of AI engagements. We don't run theatrical kickoffs or hand over consultant slide decks. The deliverables are things your engineering team can act on.
Define the actual problem.
Half of AI engagements fail because the use case wasn't quite the right one. We start by mapping the workflow, the data, and the success criteria. We'll tell you if the problem doesn't need AI at all.
Pick the right tools.
Model choice, retrieval design, guardrail strategy, deployment model. We document the trade-offs in plain English and commit to them in writing before any production code gets written.
Ship with evaluation built in.
An eval harness from day one, production-grade observability, versioned indexes and prompts, cost controls, security gates in CI. Working software at the end of every two-week cycle.
Stay in until it's yours.
Hypercare during rollout, runbooks for the on-call rotation, drift detection and a retraining cadence. Knowledge transfer to your team, or a managed support agreement on the other side. Your call.
The shortlist we work from.
What we choose for AI-native engagements. We pick specifically, we'll explain why in any proposal, and we don't pretend to be agnostic.
Model providers
Retrieval & data
Orchestration & runtime
All product names, logos, and brands are property of their respective owners. Listed for identification purposes only. Apollo Technologies is not affiliated with, endorsed by, or sponsored by any of the companies named above.
Tell us about your AI project.
Send a paragraph about what you're trying to build: the use case, where the data lives, what "working" looks like. We'll reply within one business day, either with a 30-minute call or with an honest "this isn't the right fit; here's who you should call instead."