AI engineering service
Copilots that sit inside the work your team already does.
We build AI assistants embedded in your internal tools and products — grounded in your data, aware of the user's context, and designed to speed people up rather than replace their judgment.
The problem
Generic AI tools do not know your business.
Teams paste context into public AI tools, lose time switching windows, and get answers that ignore internal rules. A copilot belongs where the work is, with access to the right information.
What we typically hear
- Staff copy sensitive information into external AI tools.
- Answers are generic because the AI lacks company context.
- Switching between tools breaks concentration.
- No citations, so outputs are hard to trust.
- Customers expect AI assistance inside the products they pay for.
What we build
What we build
In-app assistants
Side panels, command bars, and inline suggestions inside your web application or internal tool.
Grounded answers
Responses drawn from your documents and data, with citations so users can check the source.
Context awareness
The copilot knows which record, page, or customer the user is looking at.
Action shortcuts
One-click actions such as drafting a reply, summarizing a record, or filling a form — confirmed by the user.
Permission inheritance
Users only see answers drawn from information they are already allowed to access.
Feedback capture
Ratings and corrections that feed back into evaluation and improvement.
Typical use cases
Where this work pays off
Support agent assist
01Suggests replies and relevant articles while an agent works a ticket.
Sales account brief
02Summarizes an account's history and open items before a call.
Internal policy assistant
03Answers HR, IT, or process questions with links to the governing document.
Product copilot
04Helps your customers use your SaaS product, generate content, or analyse their data.
Architecture
How a copilot request flows
- 1
Ask
The user asks a question or triggers an action.
- 2
Scope
The copilot applies the user's permissions.
- 3
Retrieve
Relevant records and documents are gathered.
- 4
Respond
An answer or draft is generated with citations.
- 5Human
Confirm
The user accepts, edits, or rejects.
Technology considerations
Decisions we make with you
Technology choices follow from the workflow, data, and constraints — not the other way around.
Latency
Streaming responses, caching, and smaller models for quick tasks keep the copilot responsive.
Retrieval quality
Chunking, hybrid search, and re-ranking tuned to your content so answers are relevant.
UI patterns
Interfaces that make AI output clearly distinguishable from verified data.
Multi-tenant safety
For SaaS products, strict tenant isolation in retrieval and prompts.
Delivery
How the engagement runs
01
Use-case definition
Choose the moments in the workflow where assistance saves the most time.
02
Prototype
A working copilot on real data for a small group of users.
03
Evaluate
Measure answer quality and usefulness with real questions.
04
Integrate
Embed in the product or tool with permissions and logging.
05
Iterate
Improve based on usage data and feedback.
Security & governance
Control is designed in, not added later.
Every system we deliver includes the controls below as a baseline. Specific compliance requirements are scoped with your team.
- Answers limited to data the user can already access
- Citations for every factual claim drawn from internal sources
- Logging of prompts and responses for quality review
- Opt-out and data-retention controls
- No training of third-party models on your data without agreement
FAQ
Frequently asked questions
01What is the difference between a copilot and an AI employee?
A copilot assists a person while they work and waits for them to act. An AI employee carries out tasks itself within defined limits and escalates when needed.
02Can a copilot be added to our existing SaaS product?
Yes. We regularly embed copilots into existing applications through your API and front end, respecting your tenant and permission model.
03How do you stop it from making things up?
Answers are grounded in retrieved sources with citations, the copilot is instructed to say when it does not know, and quality is measured against real questions before launch. No system eliminates errors entirely, which is why users stay in control.
Related reading
Go deeper on this topic
Related services
Often delivered together
AI
RAG & Knowledge Systems
RAG development by Neuralex Labs: retrieval-augmented AI over your documents and data with citations, access control, and measured answer quality.
AI
Generative AI Development
Generative AI development from Neuralex Labs: LLM features and pipelines with structured outputs, evaluations, guardrails, and predictable running costs.
Engineering
SaaS Product Development
SaaS product development by Neuralex Labs: multi-tenant platforms with AI features, billing, permissions, and analytics, engineered for growth.
Next step
Bring us the process your team is tired of doing manually.
A strategy call is a working conversation about one workflow: what it involves today, what could be automated, and what a first build would look like.
What happens next
- 1Tell us about one workflow
- 2We review it before replying
- 3A working session, then a first-build proposal