# NativeLayer AI

> AI enablement and product engineering for teams that ship. From Texas to the world. We help teams decide which AI use cases are worth building, build them to production quality inside the client's own infrastructure on Google Cloud, and hand over systems the client's engineers can run.

- Website: https://nativelayer.ai
- Contact: https://nativelayer.ai/contact (free 30-minute scoping call) or hello@nativelayer.ai
- Full site text for assistants: https://nativelayer.ai/llms-full.txt
- Spanish version of the site: https://nativelayer.ai/es (legal pages are English only)
- Updated: 2026-10-14

## Services

- [AI Enablement](https://nativelayer.ai/services/ai-enablement): A fixed-price assessment that ranks your AI use cases, then a pilot that puts the first one in production with evaluations, security review, and your engineers trained to run it. Format: Fixed-price assessment, then a milestone-priced pilot that takes one use case to production. Timeline: 2–3 weeks assess, ~4–8 weeks pilot. Team: 1 lead engineer + 1 senior engineer.
  - A prioritized roadmap of AI use cases ranked by value and feasibility
  - A readiness assessment covering data, security, and team skills
  - Production integration of LLM and agent workflows into your product
  - Evaluation harnesses so quality is measured, not guessed
  - Your engineers trained to own and extend the system
- [Product Engineering](https://nativelayer.ai/services/product-engineering): A lead and one or two senior engineers build your product in two-week iterations, each ending in a demo you can use, with tests, CI/CD, and runbooks included. Format: Scoped build with a demo every two weeks; milestone or time-and-materials pricing with a weekly cap. Timeline: Typically 6–16 weeks per product phase. Team: 1 lead + 1–2 senior engineers.
  - A shipped product or feature, not a prototype
  - Clean architecture your team can extend without us
  - Automated tests and CI/CD from the first commit
  - Observability, alerting, and runbooks before launch
- [Platform & Data](https://nativelayer.ai/services/platform-data): An audit of what exists, then the smallest Google Cloud platform that fits, with infrastructure as code, data pipelines with lineage, and a versioned, reversible way to deploy models and prompts. Format: Audit first, then an infrastructure-as-code build with handover sessions for your platform owners. Timeline: Typically 3–10 weeks. Team: 1 cloud architect + 1 data/ML engineer.
  - A cloud architecture sized for your actual traffic and budget
  - Data pipelines with lineage, quality checks, and access controls
  - Model and prompt deployment with versioning and rollback
  - Cost and latency dashboards your team reviews weekly
- [All services](https://nativelayer.ai/services)

## How engagements work

1. Assess (2–3 weeks, fixed price): map workflows and data, test feasibility against real data and security constraints, deliver a ranked roadmap with effort and risk per use case, or a written no. See https://nativelayer.ai/services/ai-enablement
2. Pilot (typically 4–8 weeks): one use case to production with evaluations in CI, security review, monitoring, and a runbook; a demo every two weeks. See https://nativelayer.ai/services/product-engineering
3. Scale (ongoing): further two-week iterations only while they earn their keep; the client's engineers pair with ours until they run it alone. See https://nativelayer.ai/services/platform-data

Teams are 1–3 senior engineers; the people on the kickoff call write the code. The client owns all code, infrastructure, and documentation from the first commit.

## Published commitments

- Reply to inquiries within two business days.
- The assessment is fixed scope and fixed price, two to three weeks.
- Teams of one to three senior engineers.
- The client owns code, infrastructure, and documentation from day one.
- No analytics or advertising cookies on this site; form data is retained for 24 months; governing law is Texas, United States.

## Not a fit

- A staffing bench or a managed service desk: Our format is one to three senior engineers who pair with yours and hand over. A bench needs layers; a 24/7 desk needs a rota. Neither is what we are good at.
- A demo for a board meeting: A demo has no user, so it cannot have an evaluation suite, and no AI feature we build is done without one. If the goal is a decision, the assessment gives the board a ranked roadmap or a written no instead.
- Work without access to the systems it depends on: Retrieval, evaluation and monitoring all need the real data path. Without access we would be guessing, and we would rather say so than invoice for it.
- A chatbot over content nobody maintains: Retrieval is only as good as the corpus. If no one owns the content, the answers rot and the evaluation suite shows it.
- “AI” where a lookup table or a rule would do: If a correct answer can be written as a rule, a rule is cheaper, faster, testable and reversible. The assessment says so in writing when that is the case.
- Autonomous actions with no reversal path: An agent that can act must have a human approval point or a rollback. We scope an agent’s tools and approval points before it reaches production.
- A pilot with no named user: Without one person who will use the result weekly there is no feedback loop, no evaluation data and no honest demo every two weeks.

## Resources

- [How we work](https://nativelayer.ai/how-we-work)
- [The assessment](https://nativelayer.ai/assessment)
- [The first call](https://nativelayer.ai/first-call)
- [Use-case ranker](https://nativelayer.ai/tools/use-case-ranker)
- [Definition of done](https://nativelayer.ai/definition-of-done)
- [Security](https://nativelayer.ai/security)
- [What we say no to](https://nativelayer.ai/no)
- [Glossary](https://nativelayer.ai/glossary)
- [FAQ](https://nativelayer.ai/faq)
- [Definition of done, Markdown](https://nativelayer.ai/DEFINITION_OF_DONE.md)

## Glossary

- Assessment: A 2–3 week fixed-scope, fixed-price engagement that ends in a ranked roadmap of AI use cases with effort and risk per item, or a written no.
- Audit: How platform work starts: what exists, who can operate it, what it costs, and where product or AI work is blocked. Ends in the smallest platform design that removes the blockers.
- Pilot: One use case taken to production, typically in 4–8 weeks, with an evaluation suite in CI, security review, monitoring and a runbook. A demo every two weeks.
- Two-week iteration: The unit of work after the pilot. Each ends in a demo in your environment and continues only while it earns its keep.
- Evaluation suite: A set of real cases with expected answers, run in CI against every prompt, model or retrieval change. It is how “better” is measured instead of guessed.
- Golden set: A starting set of real cases with the correct answer written down, drawn from the work of the people who will use the result.
- Retrieval: Looking up your own documents or records to ground a model’s answer. We use it where it earns its keep, which the evaluation suite decides.
- Agent: A model that calls tools in a loop with a stopping rule. Anything else is a workflow. We scope an agent’s tools and approval points before it reaches production.
- Guardrail: A check between the model and the world: input validation, output constraints, a human approval, or a rule that refuses. Measured in the evaluation suite.
- Prompt version: Prompts are deployed like code: versioned and reversible. A prompt change is a release.
- Rollback: Returning to the previous model, prompt or infrastructure version. Versioned, reversible, and documented in the runbook.
- Runbook: The document the person on call reads: what each alert means, the first action, the rollback command, who to call.
- Infrastructure as code: Your cloud environment described as code, usually Terraform, so it can be rebuilt, audited and rolled back.
- Decision record: One short document per model, prompt or data decision: what was chosen, what was rejected, and the evidence. Written so your team can defend it later.
- Written no: The assessment’s other outcome: the use case, the blocker, and what would have to change. Delivered in writing, as the assessment’s result.
- Handover: Not a phase at the end. Code, infrastructure and docs are in your accounts from the first commit, and your engineers pair with ours until they run it alone.
- Milestone pricing: Pilot or scale work quoted per milestone. Scope is written down before work starts.
- Time and materials with a weekly cap: The alternative to milestones: time and materials with a weekly cap, reviewed at every two-week demo.

## About

- What: AI enablement and product engineering services
- Where: Texas, United States, working remotely with clients worldwide (from Texas to the world)
- Starts with: a 2–3 week fixed-price assessment
- You own: all code, infrastructure, and docs, from day one
- Contact: hello@nativelayer.ai
- More: https://nativelayer.ai/about

## Frequently asked

### How long does an engagement take?
An assessment runs two to three weeks and ends with a ranked roadmap. A pilot that takes one use case to production typically takes four to eight weeks. Scaling work continues in two-week iterations for as long as it earns its keep.

### Who actually does the work?
A lead engineer plus one or two senior engineers. The people on the kickoff call write the code. We do not hand off to a separate delivery team.

### How do you price work?
The assessment is fixed scope and fixed price, agreed up front. Pilot and scale work is quoted per milestone or on a time-and-materials basis with a weekly cap, whichever fits your budgeting. No retainer is required to start or to stop.

### How do you handle security and data?
Work runs in your cloud project under your identity provider, logging, and access controls. Model and prompt choices are reviewed for data-handling implications before anything reaches production, and every decision is documented so your team can defend it later.

### Why not hire in-house or use a larger consultancy?
The engineers on the kickoff call write the code, and your engineers pair with them, so you are not dependent on us afterwards. If you can hire the team you need in the time you have, do that; the assessment will say so.

### What do you need from us?
Access to the systems the work depends on, a named person who would use the result, and someone who can decide security questions. Without these the work slows down, and we will tell you.

### What happens after handover?
Code, infrastructure, and docs have been yours from day one, so nothing transfers at the end. You can run it yourselves or ask for further two-week iterations. Neither is required.

### What if we are not sure AI is the right answer?
Then the assessment is the right first step. Part of its job is to say no to use cases that will not pay off, and we will tell you if a conventional solution is the better fit.

### What does the AI readiness assessment deliver? (AI Enablement)
A ranked roadmap of use cases with effort and risk per item, a readiness review of your data, security, and team skills, and a recommendation on what to build first, or a written no. It is fixed price and takes two to three weeks.

### Do we own the code and the models you integrate? (AI Enablement)
Yes. Code, infrastructure, prompts, and evaluation suites live in your repositories and your Google Cloud project from the first commit. Nothing transfers at the end because nothing ever left.

### Which models and platforms do you integrate? (AI Enablement)
Whatever fits the use case and your constraints. We work with hosted model APIs and Vertex AI, and we choose retrieval, tools, and guardrails per use case, with an evaluation suite that shows the difference.

### Can you work with our existing engineering team? (Product Engineering)
Yes. We take a clearly scoped product line or platform piece, pair with your engineers in the same repositories, and leave code, tests, and docs they already understand.

### How is product engineering priced? (Product Engineering)
Per milestone, or time and materials with a weekly cap, whichever fits your budgeting. Scope is written down before work starts and reviewed at every two-week demo.

### Which stacks do you build with? (Product Engineering)
TypeScript across the board, React or Astro on the web, React Native or native on mobile, Node or Go services, Postgres, and Google Cloud or Firebase for hosting and data.

### Do you only work on Google Cloud? (Platform & Data)
Platform work is Google Cloud and Firebase. If your data and systems are elsewhere and cannot move, the assessment will say so before anyone commits to a build.

### What does the platform audit cover? (Platform & Data)
What exists today, who can operate it, what it costs, and where AI or product work is blocked. It ends with the smallest platform design that removes the blockers, as infrastructure as code.

### Who operates the platform afterwards? (Platform & Data)
Your platform owners. Handover sessions, Terraform, runbooks, and dashboards are part of the build, and the work is in your project under your identity provider from day one.

## Optional

- [Privacy](https://nativelayer.ai/privacy)
- [Terms](https://nativelayer.ai/terms)