Production AI systems your operators actually trust

WebCoreLab ships LLM ops, vector search, and retrieval augmented generation that survive Monday morning traffic. We build for US brands that need real numbers, not demos.

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The work, plainly

What AI engineering actually means

Most teams do not need a research lab. They need a small, opinionated group that ships a working pipeline, instruments it, and stays on call when the model drifts.

AI engineering is the boring, valuable part: data ingestion, retrieval, evaluation harnesses, prompt versioning, fallback paths, cost ceilings, observability. We treat models the way DevOps teams treat services. Versioned, monitored, rolled back when a release misbehaves.

Our team has shipped a fine-tuned Falcon LLM behind a US support desk, a Pinecone-backed search layer for a 38k-SKU catalog, and a Klaviyo predictive analytics integration that re-scored 1.4M contacts on a nightly job. None of it shipped on day one. All of it ships now.

Capabilities

Three pillars, built to interlock

Pick one to start. Most clients add the next two within a quarter because the data and the eval harness already exist.

LLM ops & evaluation

Versioned prompts, golden test sets, regression alerts, prompt diff reviews in PRs. We treat LLM ops like any other production service: SLOs, dashboards, on-call. You get a model that can be safely changed.

Vector database design

Pinecone, Qdrant, or pgvector. We pick by query pattern, not hype. Includes schema for metadata filters, hybrid BM25+dense ranking, and a re-embedding pipeline that survives a model swap.

Retrieval augmented generation

RAG that quotes its sources, refuses out-of-scope questions, and logs every retrieval call. Built with chunking strategies tuned to your content type: product, policy, ticket, transcript.

Where it earns its keep

Three programs, three measurable outcomes

These are the ones we get asked about every week. Pick the one closest to your roadmap.

E-commerce predictive analytics

Klaviyo predictive analytics tuned with your first-party order data, refund signals, and on-site events. We backtest LTV and churn windows before any send, then hand off a Klaviyo flow your CRM team can edit.

Generative AI customer support

A retrieval-grounded agent that handles tier-1 tickets, escalates with clean context, and writes a draft reply for tier-2 reviewers. The refund policy is in the index, not the prompt.

Generative AI for content

Briefs, outlines, and first drafts produced inside your style guide. Ships through an editor queue, not direct-to-publish. Useful for product copy at scale, FAQ expansion, and programmatic SEO pages.

Stack

Tools we ship with

Vendor-pragmatic. What we run in production today. We swap parts as the market moves and the eval scores tell us to.

OpenAI
Anthropic Claude
Llama 3
Falcon LLM
Pinecone
Qdrant
pgvector
LangChain
LlamaIndex
Klaviyo
Segment
Snowflake
AWS Bedrock
LangSmith

How we work

Four phases, no theater

Same shape every time. The names are dull on purpose because the work is technical, and dull names ship better than clever ones.

Phase 01 · Discovery

Two-week scoping. We read your data, your tickets, and your last three quarterly plans. You get a written architecture doc and a fixed POC quote.

Phase 02 · POC

A working pipeline against real data, with an eval harness and a baseline score. If the numbers do not beat the baseline, we say so before you spend more.

Phase 03 · Production

Hardening: rate limits, fallback paths, cost ceilings, audit logs, role-based access. Deployed to your cloud account or ours, your call.

Phase 04 · Monitor

Weekly eval runs, drift alerts, prompt regression reviews, monthly retro. Either party can end the engagement at 30 days notice.

FAQ

Questions we hear in the first call

Do we need a vector database, or will Postgres work?

For most US mid-market teams, pgvector inside an existing Postgres instance is the right starting point. We move to a dedicated vector database (Pinecone or Qdrant) once query volume crosses roughly 50 QPS or hybrid ranking becomes a bottleneck.

When does fine-tuning beat retrieval augmented generation?

Fine-tuning wins on tone, format, and narrow classification tasks where the desired output is predictable. RAG wins when the answer must cite a source or change the moment a document is updated. Most production systems we ship use both: a fine-tuned base for voice, RAG for facts.

Can you work with our existing Klaviyo and Segment stack?

Yes. Klaviyo predictive analytics, Segment events, and Snowflake warehouses are our usual data plane. We add the model layer and the eval harness, and we keep your data engineers in the loop on schema decisions.

How do you measure that the model is actually working?

Every project gets an eval harness on day one. We define golden test sets with your team, score every prompt change, and flag regressions in the same channel as your other CI checks. No model goes to production without a passing baseline.

What does a typical POC cost and how long does it take?

Discovery is two weeks at a fixed rate. A focused POC runs four to six weeks and ends with a written go/no-go recommendation. Production phase is sized to the scope. We share specific numbers in writing after the discovery call.

Will you train our internal team?

Yes. Every production handoff includes runbooks, a recorded architecture walkthrough, and two paired-engineering weeks where your developers ship changes with us watching. The goal is that we are optional by month six.

Talk to an engineer

Bring a real problem. We will give you a real plan

Book a 30-minute discovery call. You leave with two things: an honest read on whether AI engineering services are the right move right now, and a one-page sketch of what the first POC would look like. No deck. No follow-up nurture sequence.

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See past work