What we do

AI Engineering Services

Neradot builds AI search and agent systems for teams that already have real data and real users.

AI & Search Architecture Audits

For a new build or a system already in production. We read the whole answer path — prompts, chunking, embeddings, retrieval, ranking, and the cluster underneath — and tell you which layer is actually costing you the answer.

  • Feasibility on something new: whether the use case survives contact with your real data, what it costs, and what it takes to keep running.
  • AI search health on something live: prompt design, chunking strategy, embedding choice, retrieval and reranking — measured against your queries, not eyeballed.
  • Infrastructure where it earns attention: index mappings, analyzers, and query shapes, checked as one layer of the path rather than the whole story.
  • Production roadmap, sequenced: cheap wins first, the rewrite last, with an honest estimate on both.

Call us whenYou have been quoted six months for something that smells like six weeks — or the answers are wrong and nobody can say which layer is lying.

AI Search & Retrieval Infrastructure

Most “the AI is wrong” bugs are retrieval bugs. We build the layer that puts the right documents in front of the model, every time, at your traffic.

  • Hybrid search: BM25 for the exact terms people type, vectors for what they meant, fused and weighted against your own queries.
  • Elasticsearch, OpenSearch, Pinecone, pgvector — any semantic search database, any model provider. Picked for your scale and the team that has to operate it at 3am.
  • Reranking pipelines, chunking strategy, and query understanding, measured on your traffic rather than a public benchmark.

Call us whenRecall looks fine in the notebook and falls apart in the product, and nobody can tell you where the answer went.

Read: Hybrid search — BM25, semantic search, or both

Production AI Agents & Automated Workflows

Agents that do the work, not agents that demo well. Stateful, observable, and safe to interrupt when something goes sideways.

  • ReAct loops and tool calling with real failure handling: retries, timeouts, budgets, and a plan for when the model is confidently wrong.
  • Knowledge pipelines that keep ingestion, enrichment, and indexing running without a person babysitting them.
  • Human-in-the-loop checkpoints exactly where being wrong is expensive — and nowhere else, so review stays a decision and not a queue.

Call us whenThe prototype works seven times out of ten, and seven is not a number you can put in front of customers.

Read: Question extraction as a retrieval strategy

Cost & Latency Optimization

The system works. The invoice and the p95 do not. Both are engineering problems with known fixes, and they are usually the same fixes.

  • Semantic caching and token pruning — typically the first large cut off the monthly bill, before anything is rearchitected.
  • Model routing: the small model takes the easy majority, the expensive one earns its price on the rest.
  • Vector index quantization and retrieval tuning to pull memory, cold starts, and tail latency back into budget.

Call us whenFinance has started asking about the inference line item, or your p95 has a tail nobody can explain.

Case study: 36% off the bill with prompt caching

Evals, Benchmarking & Regression Testing

You cannot improve what you only feel. We build the harness that turns “it seems better” into a number that moves, then wire it into the pipeline.

  • Offline evaluation harnesses with golden sets built from your traffic and labelled by people who know your domain.
  • Hallucination, groundedness, and retrieval scoring reported per change, so a regression has an address.
  • CI/CD quality gates, so a one-line prompt tweak cannot quietly undo last quarter's fix.

Call us whenEvery model upgrade is a leap of faith, and shipping depends on whoever clicked around the demo last.

Before you ask

How does an engagement usually start?
With a “speedy audit.” We pick one goal that can deliver real value, then spend the first four to six weeks going deep on both the technology and the business — and hand back something you can actually use at the end of it. Not a proof of concept: a small MVP, built to be extended.
Do you work on existing systems or only new ones?
Both, and they are different jobs:
  • Greenfield — the question is what to build, whether your data supports it, and what the smallest version that delivers value looks like.
  • Live systems — something already ships and has stopped behaving: wrong results, climbing cost, latency nobody can account for.
Which search stacks and models do you work with?
We pick what your team can operate, not what wins benchmarks in a blog post:
  • Search and vector databases — Elasticsearch, OpenSearch, Pinecone, pgvector, or any other semantic search database, with hybrid BM25 and vector retrieval plus reranking on top.
  • Models and providers — Cohere, OpenAI, Amazon Titan Text Embeddings, and open models such as BGE-M3 or E5 when the data cannot leave your network.
What happens when the project ends?
One of three things, and it is your call:
  • Handoff — your engineers get the system, the eval harness, and the documentation that keeps both honest.
  • Maintenance — we stay on for model upgrades, drift, and the quarterly review.
  • Expansion — the first system earned its keep, so we build the next one on top of it.
How long until something reaches production?
The speedy audit ends with a working MVP in four to six weeks. From there, six weeks is our median from MVP to production. Company-wide rollouts run longer and get staged, so value ships before the whole thing is finished.

Not sure which one you need?

That is usually the audit. Tell us what is misbehaving and we will tell you what it will take.

Let's Talk