AI isn't magic—it's a data problem. We build the foundations that make AI actually work: grounded in your enterprise data, governed for production use, and designed to deliver measurable value rather than demos that never ship.
"Most AI projects fail not because of the model—but because the data foundation isn't there."
Applied AI is the work of turning AI from a demo into a system that runs in production. AstroSpider grounds AI in your enterprise data, governs it for safe production use, and builds the data foundations—context engineering, retrieval, deterministic lookups, MLOps, and governance—that make it deliver measurable value rather than demos that never ship.
Build retrieval-augmented generation systems that connect LLMs to your enterprise content. Users get accurate, source-backed answers with citations—not hallucinations. We design for access control, traceability, and quality evaluation from day one.
Prepare your data for AI and ML workloads. We build semantic layers, feature stores, and curated datasets that models can actually use—with the data quality, consistency, and documentation that production AI requires.
Implement semantic search infrastructure for similarity matching, recommendations, and RAG retrieval. We configure embedding models, optimize vector indexes, and design hybrid search strategies that balance precision with recall.
Build the infrastructure to deploy, monitor, and manage models in production. We implement model registries, serving endpoints, A/B testing, and the CI/CD pipelines that take models from notebook to production reliably.
Connect large language models to your enterprise systems and workflows. We handle prompt engineering, API integration, response parsing, and the guardrails needed to use LLMs safely in business-critical applications.
Implement frameworks to test, monitor, and improve AI systems over time. We build evaluation pipelines that measure answer quality, detect hallucinations, and track regression as your content and models evolve.
Retrieval-augmented generation made search cheap for models. The hard part was always ingestion: knowing what is in your content before anyone asks. So we start from the outcome each question needs and use the simplest pattern that answers it reliably. Sometimes that is retrieval. Often it is not.
Facts that must be exact, such as limits, fees, dates and counts, are extracted once at ingestion into a structured model and served by a named lookup, with their source and how fresh they are. If a fact can't be given, the system says so instead of searching for something close.
We decide what the model sees, not just what it can search. Every document is tagged at ingestion with where it applies, whether it is still current, and who may see it, so the search is narrowed and access is enforced before a single passage is ranked.
Where search is the right tool, we build it properly: layout-aware parsing that keeps tables and section structure intact, hybrid keyword and vector search, query rewriting, re-ranking, and evaluation that proves each question reaches the right source.
Some answers live in a whole document, not a passage. There the model works the way a researcher does: it searches, opens and reads, makes more than one pass, and has sub-agents summarize what they found. Each pass costs time and tokens, so we use it where depth matters more than speed.
When an answer has to be right every time, we look at the process before the model. A policy that changed yesterday is a process problem first: who records the change, and when the system learns of it. No model can keep a fact current that no process owns.
Policies, procedures, onboarding docs—searchable and answerable with source citations.
Field definitions, lineage, "where does this come from?"—for analysts and engineers.
Faster answers for your team, fewer Slack pings, grounded in your actual documentation.
Source-backed responses for support and operations with full audit trails.
Retrieval-Augmented Generation (RAG) is an AI technique that grounds a language model's answers in your own trusted data. Instead of relying on what the model remembers, it retrieves the most relevant passages from your documents at question time and answers from those - so responses stay accurate, current, and traceable to a source.
Super Spider Bot answers only from a curated, source-grounded knowledge corpus - not the open web - and cites the source of each answer. It combines context grounding, keyword routing, deterministic lookups for facts that must be exact, and retrieval-augmented generation that is filtered by access and currency before anything is ranked. It says 'not documented yet' instead of guessing. That grounding and traceability is what separates it from a generic chatbot.
No, but it is no longer the default answer to every question. RAG is one way to give a model context, and it is good at finding relevant passages. Facts that must be exact are better extracted once and looked up deterministically, and questions that need a whole document are better served by an agent that can open and read it. Most production systems combine all three, and choosing between them for each kind of question is the design work.
Context engineering is the discipline of deciding what a language model sees when it answers: which sources, which version, filtered to what the person may access, and in what form. RAG is one context-engineering technique. Ingestion-time enrichment, query routing, deterministic lookups and agentic retrieval are others.
If you can point us to the content your teams already rely on—policies, docs, model definitions, tickets—we can design a pilot that demonstrates value fast, without compromising governance. Let's figure out what AI can actually do for your organization.
Start the Conversation