Embeddings & vector search
Turn your content into embeddings and semantic search over a vector database that understands meaning.
Backend Development · AI-First · Results-Driven
AI & Data Services,Built Into Your Backend
Short answer: AI and data services are the backend components that let an application use machine learning and large language models — embeddings and vector search for semantic lookup, RAG so a model answers from your own documents, model APIs for generation, and the data pipelines that feed them. The engineering is mostly plumbing: getting clean data in, calling models reliably, and grounding answers so they're accurate. We build these services into your backend so AI features run on your real data, safely and cost-consciously, rather than as a bolted-on demo.
AI & Data Services That Run on Your Data
AI and data services are the backend layer behind features like a chatbot that answers from your documentation, semantic search that understands meaning rather than keywords, or automatic summaries and extraction. Underneath, they're a specific kind of backend work: turning your content into embeddings, storing them in a vector database, retrieving the right context, and calling a language or other model API to produce an answer. The visible AI feature is small; the reliable data plumbing behind it is most of the job.
The decisions that decide whether an AI feature is trustworthy are grounding, data quality and cost control. Retrieval-augmented generation (RAG) grounds a model's answers in your own approved content so it stops making things up; clean, well-chunked source data is what makes retrieval accurate; and caching, model choice and token budgets keep the bill sane at scale. The common failures are hallucinated answers with no source, a vector index built on messy data, prompts and keys leaking, and costs that spiral because nothing is measured. AI features also need evaluation, not just a demo that looked good once.
We build AI services as real backend systems: pipelines that keep your data current, retrieval that cites its sources, model calls behind clean APIs with retries and rate handling, and monitoring on both quality and cost. AI automation is our flagship focus, and we're model-agnostic — we use the provider and approach that fits your data, privacy needs and budget, not whatever is trending.
What we cover
What We Build in AI & Data Services
RAG pipelines
Retrieval-augmented generation so a model answers from your own documents and cites its sources.
Model & LLM API integration
Reliable calls to language and other model APIs behind clean endpoints, with retries and rate handling.
Data pipelines for AI
Ingestion, cleaning and chunking pipelines that keep the data feeding your models fresh and accurate.
Semantic & hybrid search
Search that blends keyword and vector matching for results that are both relevant and exact.
Evaluation & cost monitoring
Quality evals, guardrails and token/cost tracking so AI features stay accurate and affordable.
Services
AI & Data Services
From a first RAG chatbot to a monitored, cost-controlled AI layer inside your product — grounded in your own data.
RAG chatbot backend
The retrieval and model plumbing behind a chatbot that answers from your documents, with citations.
Embeddings & vector search
Build the embedding pipeline and vector index that power semantic search and recommendations.
LLM API integration
Add generation, summarization or extraction to your product behind reliable, monitored endpoints.
AI data pipeline
Ingestion and preprocessing that keeps your source data clean, chunked and current for models.
Model serving & inference API
Serve models behind an API with caching, batching and autoscaling for production traffic.
AI feature hardening
Add guardrails, evaluation, prompt-injection defenses and cost controls to an existing AI feature.
Tools & platforms
The AI & Data Stack We Build With
The exact toolset depends on your goals — these are the platforms we use most, and we work with whatever your team already relies on.
Models & APIs
AI Frameworks
Data Pipelines
Apache AirflowdbtKafkaPandasunstructured.io
Chosen per project — not a fixed menu. Have a preferred tool or platform? We’ll work with it.
Built to last
Secure, Accessible & Built to Last
We build on modern, well-supported frameworks with security and accessibility baked in — dependency hygiene, input validation, HTTPS and WCAG-minded UI — so your product is safe and usable from day one.
You own all the code and assets. Everything ships with documentation and a clean handover, so your team (or ours) can maintain and extend it without lock-in.
Who we work with
Industries We Build For
20+ years across sectors — in Houston and internationally.
Transparent pricing
Estimate your project in seconds
Pick what you’re building for an indicative range, then request an exact quote. No email wall.
Simple prices for typical tasks
- API buildfrom $8k
- Backend + DBfrom $15k
- Integrationsfrom $10k
- Care planfrom $800/mo
Proof
700+ projects, 20+ years
See the products and growth work we’ve shipped across industries — and request a case study relevant to yours.
How we work
Fixed scope. Sprints. Working software.
- 01
Scope & fixed estimate
A short discovery call turns your idea into a clear spec and a firm range — free.
- 02
Design & architecture
UX, data model and stack chosen for your scale, not ours.
- 03
Build in sprints
Working software every 1–2 weeks — you see progress, not promises.
- 04
Launch & scale
We ship, measure and keep improving with care plans.
FAQ
AI & Data Services questions
How much do AI and data services cost?
A focused AI feature like a RAG chatbot or semantic search typically starts around $10,000, while a broader AI data platform with pipelines, evaluation and serving can run $40,000 or more. There's also ongoing model/API usage cost. Use the estimator for a build range, then book a free call for a firm quote.
What are AI and data services in a backend?
They're the server-side components that let your app use AI — embeddings and vector search for semantic lookup, RAG so a model answers from your own content, model/LLM APIs for generation, and the data pipelines that feed them. Most of the work is reliable data plumbing, not the model itself.
How long does it take to build an AI feature?
A grounded RAG chatbot or semantic search feature usually ships in 4–8 weeks; a larger AI data platform with pipelines, evaluation and serving runs 2–4 months. We start with a small, evaluated slice on your real data before scaling it.
What is RAG and why does it matter?
RAG — retrieval-augmented generation — retrieves relevant passages from your own documents and gives them to the model as context, so answers are grounded in your approved content instead of the model guessing. It's the main technique for making an AI assistant accurate and reduces hallucination and made-up facts.
Will our data be used to train someone else's model?
Not if we design it that way. We use providers and configurations where your data isn't used for training, and for sensitive cases we can run open-source models in your own environment. We'll choose the approach that fits your privacy and compliance needs and confirm the specifics on a call.
Can you add AI to our existing product?
Yes. Most AI work sits on top of an existing app — we add the embedding pipeline, retrieval and model endpoints against your current data and backend, so you get an AI feature without rebuilding the product.
How do you keep AI features accurate and control the cost?
We ground answers with RAG and citations, add evaluation and guardrails so quality is measured rather than assumed, and control spend with caching, model selection and token/cost monitoring — so the feature stays both trustworthy and affordable as usage grows.
Do you build AI and data services for clients outside Houston?
Yes. We're based in Houston, TX and deliver AI and data engineering to clients nationwide and internationally — across 20+ years we've delivered 700+ projects, with AI automation as our flagship focus, for local and remote teams alike.
Get started with AI in your backend
Book a free consultation — we'll scope your AI or data feature and give you an honest range.
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