AI engineering for software and SaaS teams, across evaluation, retrieval, cost, security and the release itself.
The right-hand column is what gets discovered in the last month before launch, when it is most expensive to fix.
In brief
EigenSpark builds AI features and the engineering around them for software and SaaS companies. We put an evaluation harness around the feature so every change is measured before it ships. We make retrieval respect the permissions your product already enforces. We get the cost per request down to a number your pricing can carry. Then we hand the whole thing to your engineers, in your repository.
What we cover
The controls and evidence your enterprise buyers check before they sign.
The starting position
Nothing is measured, retrieval leaks, the economics were never modelled, and the provider changes.
Changes ship on the strength of ten prompts someone tried by hand. Without an evaluation set, every change feels risky.
The index knows everything and the user should not. Filtering after retrieval is the usual shortcut, and it is a security bug.
Token spend per user, per plan and per feature, with retries and long contexts, is usually discovered after launch. It costs margin.
A version is deprecated, a default changes, latency doubles. Without pinning and replay tests you are downstream of someone else.
How an engagement runs
Make it measurable, then make it safe and affordable, then hand it to your team.
An evaluation set built from your real queries and real documents, a regression suite that runs in your pipeline, and a baseline on what you have today. Until this exists every other decision is an opinion.
Permission-aware retrieval tested as a security property, personal data kept out of prompts, embeddings and logs, and the cost per tenant modelled with routing and caching before it is a board conversation.
Your engineers work alongside ours throughout, and the handover is the point. The harness, the pipeline and the abstraction are yours, and the documentation is written for the person who joins next year.
Use cases
Shipping the feature, then quality and cost, then the product surface, then the back office.
Tenant isolation and per-user permissions enforced inside the retrieval path itself, with a test suite that treats a cross-tenant leak as a failing build.
Generative AI →Tool use, planning and multi-step execution with the approval point placed where your risk sits, plus the state and retry work that makes an agent usable in production.
Agentic AI →Search that handles the words your users actually type, combining keyword and semantic matching, measured against a relevance set built from your own query logs.
Generative AI →An evaluation set from your own traffic, graded automatically where that is honest and by people where it is not, wired in so a regression fails the build.
Full-Stack Development →Cost attributed per tenant, per plan and per feature, with model routing, caching and context discipline, so the feature has economics you can show a board.
Cloud & AI Infrastructure →Defined behaviour when a provider is slow, rate limited or down: a timeout, a smaller model, a cached answer or an honest message, chosen and tested in advance.
Cloud & AI Infrastructure →An assistant grounded in your own documentation, data and permissions, that cites what it used and hands off cleanly when it cannot answer.
Generative AI →Extraction your customers can point at their own documents, with confidence per field, a review screen, and an accuracy target set during onboarding.
Document Intelligence Engine →Questions turned into queries against your own schema, with the query shown, row-level permissions applied and the result explained, so a wrong answer is visible.
Data Engineering & Platforms →Tickets classified, routed and answered from your own documentation and past resolutions, with thresholds set so the assistant hands over early.
Agentic AI →Product telemetry, support history and billing read into account-level signals your customer success team can act on, with the reasoning behind each flag shown.
AI & Data Strategy →Assisted development, review and test generation introduced where they help, with the effect measured on cycle time and defect rate.
Full-Stack Development →The engineering half
The model is the easiest part to swap. These four decide whether the feature ships.
Isolation and permissions belong inside retrieval. Filtering after the fact is the shortcut that fails a security review.
An abstraction over providers, with open-weight options where the data or the economics need them, so a deprecation is a config change.
The layer almost nobody builds, and the one that decides how fast the team can move afterwards.
Unit economics and audit evidence, treated as engineering requirements from the start.
We also run the training that goes with it: retrieval design, evaluation, agentic patterns, LLM security and the operational side, for engineers who will own this after we leave.
What we offer software and SaaS teams
Most product teams start with evaluation and retrieval, and use three of the four.
Where most product teams start.
Where a feature can be bought.
For engineers who will own this.
The teams that own this work.
Working in a specific function? See how we help Data, Analytics & IT teams.
Engineering standards
Isolation is tested, every change is measured, the code is yours, and cost is a number.
Tenant and permission boundaries are enforced inside retrieval and covered by tests that fail the build. A cross-tenant leak is treated as a security incident in the test suite before it can be one in production.
Every change to a prompt, a model, a chunking strategy or a retrieval parameter runs against the evaluation set. An improvement is a number, and so is a regression.
The work happens in your codebase, with your engineers, under your review process. The harness, the abstraction and the runbooks are deliverables, and the documentation is written for whoever joins next year.
Token spend per tenant and per plan is modelled before architecture is settled, because the cheapest time to fix unit economics is before the feature is built.
FAQs
Usually for one of three reasons. You want an evaluation harness and a permission-aware retrieval design done properly once, so your team can move fast on top of it afterwards. Your team is capable and fully committed to the roadmap. Or you want somebody who has already made the expensive mistakes on tenancy, cost and provider abstraction. We work inside your repository with your engineers, and the arrangement is designed to end.
Because it is the thing that decides how fast everything after it goes. A team without an evaluation set is shipping on the strength of ten prompts somebody tried by hand, so every change feels risky and the pace drops. Once a regression suite runs on every commit, prompt changes, model swaps and chunking experiments become ordinary engineering.
Isolation and permissions are enforced inside the retrieval path, so a document that a user cannot see is never a candidate. Filtering results after retrieval is the common shortcut and it fails, because an excluded chunk can still shape an answer through reranking or context assembly. We treat a cross-tenant leak as a failing test, and that suite runs on every build.
Usually, and how much depends on how it was built. The levers are routing cheaper models to the requests that do not need the expensive one, caching aggressively where the answer is stable, context discipline, and moving high-volume paths to open-weight models you host. The first step is attribution: cost per tenant, per plan and per feature, which most teams have never had.
Both, and the choice is an engineering decision made per workload. Hosted frontier models earn their cost on hard, low-volume reasoning. Open-weight models you host tend to win on high-volume, well-defined tasks, on anything where data cannot leave, and on anything where the unit economics have to work at scale. The abstraction is built so that decision can change later.
We build for it from the start. Access enforced in retrieval, personal data kept out of prompts and embeddings, deletion that actually reaches the vector store, residency as configuration, and logging that answers who retrieved what and what the model was asked. Your auditors and your counsel own the certification itself.
No, and the parts that are not are usually what makes the AI work. The retrieval path, the permission model, the evaluation pipeline, the provider abstraction and the cost telemetry are ordinary engineering, and they are most of the effort. The model is frequently the easiest component to replace.
Yes. For a team with strong engineers who need the specific skills, our AI and machine learning, cloud and data tracks cover retrieval design, evaluation, agentic patterns, LLM security and the operational side. For several companies this size that has been the better investment, and we will say so when we think it is.
Related
The three services this ships as, and the pillar they sit under.
Retrieval, grounding and the evaluation around it.
→ ServiceTool use, planning and the approval points that matter.
→ ServiceRouting, caching, latency budgets and unit economics.
→ PillarThe engineering pillar this page sits under.
→ FunctionThe function that owns the platform underneath.
→ TrainingGetting your own engineers to own this properly.
→Describe what it does, what it runs on, and what is blocking the release. We call you within 48 hours and go through what we would measure first, where the tenancy and cost risks are, and what a first engagement would cover.
Talk to us