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Applied AI engineering

Production AI that does real business work.

We design, build, and deploy AI agents, enterprise RAG assistants, and generative AI applications, measured against your own test cases and running where your data is allowed to live.

For teams that lose time searching documents, moving data between systems, or trying to make an AI prototype dependable.

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Illustrative workflow — sample dataHow a knowledge assistant answers one question
  1. “What were the main support issues in these sample documents?”
  2. 3 sample sources permitted Billing records: not permitted, not read
  3. 3 excerpts selected123
  4. Sign-in fails after a password reset1, likely from the changed reset flow2; customers want clearer errors3.
  5. Every statement cites a permitted source; the restricted source was unused. Not checked: whether the documents are complete. Checks do not prove an answer is correct.
Question · sample user, support team

What were the main support issues in these sample documents?

  • Sample support tickets1Permitted“Several tickets report sign-in failures right after a password reset.”
  • Sample product notes2Permitted“Release 2.3 changed the password-reset flow.”
  • Sample feedback summary3Permitted“Customers ask for clearer error messages at sign-in.”
  • Sample billing recordsNot permitted — not read
Answer · drafted from cited excerpts

The main issue was signing in after a password reset1, probably linked to the changed reset flow2. Customers also asked for clearer error messages3.

Example review
  • Every statement cites a permitted source
  • The restricted source was not used
  • Not checked automatically: whether the sample documents are complete or current

Checks reduce risk. They do not prove an answer is correct.

Step 5 of 5 Example checks, and what they cannot prove

What we build

Three kinds of work, each with clear limits.

Which contracts renew this quarter?

Two sample contracts renew in this quarter1, and one has a notice period that starts this month2.

  • Contract A 1
  • Contract B 2
  • Renewal policy
Illustration · sample text

01

AI knowledge assistants and enterprise search

Assistants that answer questions from your documents and business data, and show the source behind every answer.

The problem
The information people need is spread across documents, shared drives, and business systems. Keyword search misses relevant material, and generic chat tools cannot reliably answer from your own content.
Typical uses
  • Questions about policies, procedures, and manuals
  • Searching contracts, reports, and research documents
  • Internal assistants for support, sales, or operations teams
You receive
  • A pipeline that ingests, splits, and indexes your documents
  • Search that combines keyword and semantic retrieval
  • Answers with citations to the passages they came from
Boundaries
Answers can only be as good as the source material. An assistant can still be wrong: citations let people check an answer, and high-stakes decisions need human review. Fully offline deployments give more control over data but can run smaller models.
Full scope and limits: Knowledge assistantsDiscuss a project like this: Knowledge assistants
  1. Request arrivesemail or form
  2. Details extractedAI, checked by rules
  3. Person approvesthe step that matters
  4. System updatedlogged

Every action is written to a log you can review.

Illustration · sample workflow

02

AI workflow automation and integrations

Software that carries out approved, multi-step tasks across your systems, with a person approving the steps that matter.

The problem
Routine work moves between email, spreadsheets, and business systems. Rule-based automation breaks when inputs vary, and unsupervised AI is not acceptable for actions with real consequences.
Typical uses
  • Triage and routing of incoming requests
  • Extracting information from documents into business systems
  • Updating records from several sources
You receive
  • A map of the workflow, marking which steps need human approval
  • Integrations with your tools and APIs, limited to the permissions each step needs
  • AI only where inputs vary; ordinary rules where they do not
Boundaries
Not every workflow benefits from an AI agent, and deterministic steps should stay deterministic. Integrations depend on the APIs and access your systems provide. Actions with financial, legal, or safety impact should keep a human approval step.
Full scope and limits: Workflow automationDiscuss a project like this: Workflow automation

Release review · example

  • Agreed test questions re-runon every change
  • Answers cite their sourceschecked automatically
  • Response time and costagainst agreed limits
  • Failures reviewed by a personbefore release
  1. Build
  2. Evaluate
  3. Deploy
  4. Monitor
Illustration · checks vary by project

03

Production AI: evaluation, deployment, and support

Taking an AI prototype into dependable use: measuring its quality, deploying it, and keeping it observable once people rely on it.

The problem
Many AI prototypes work in a demo but have no way to measure quality, no repeatable deployment, and no visibility once real users depend on them.
Typical uses
  • Reviewing an existing prototype before a wider rollout
  • Building an evaluation set and regression checks
  • Containerised deployment on AWS, Azure, or GCP
You receive
  • An evaluation dataset and automated quality checks
  • A repeatable deployment pipeline (containers, CI/CD)
  • Logging and tracing of model calls
Boundaries
Evaluation measures quality on the cases you test; it cannot guarantee behaviour on every input. Any ongoing support is agreed separately for each engagement.
Full scope and limits: Production AIDiscuss a project like this: Production AI

Also available

How a project runs

From first conversation to a working system.

Each stage ends with something you can review. Nothing is committed until scope and terms are agreed in writing.

  1. Conversation

    An initial discussion of the problem, the people affected, the data available, and what a useful result would be. It does not commit either side to a project.

    Outcome An honest view of whether AI fits the problem

  2. Design

    If there is a fit, agree the scope in writing: architecture, data access, security boundaries, and how quality will be measured.

    Outcome Scope, data access, and quality measures agreed in writing

  3. Build

    Develop the application, retrieval or workflow logic, integrations, and interface, reviewing progress with you as it takes shape.

    Outcome Working software, reviewed with you as it takes shape

  4. Evaluate

    Measure answer quality, failure behaviour, response time, and cost against the test cases agreed during design.

    Outcome Results against the test cases agreed in design

  5. Deploy and hand over

    Deploy into your environment with logging, documentation, and a runbook. Any ongoing support is agreed separately.

    Outcome A deployed system with logging, documentation, and a runbook

Who you work with

The people, and how we work.

  • Dr. Anuja Prasad S, co-owner of Travelyqor AI Systems

    Dr. Anuja Prasad S

    Co-owner & AI/ML Engineering Leader

    Generative AI, computer vision, MLOps, cloud platforms, and intelligent automation

    Full profile: Dr. Anuja Prasad S
  • Dr. Felix M. Philip, director of Travelyqor AI Systems

    Dr. Felix M. Philip

    Director · Associate Professor & Researcher

    Internet of Things, Image Processing, Smart Systems, Precision Agriculture, and Arctic Ocean Monitoring

    Full profile: Dr. Felix M. Philip

  • Evaluation before claims

    We agree test questions and acceptance criteria up front, so quality is measured rather than judged from a demo.

  • AI only where it helps

    Deterministic steps stay deterministic. We will say when a search system or ordinary software is the better answer.

  • Your data boundaries

    Cloud, private-cloud, or fully local deployment, chosen around what your data is allowed to leave.

  • Transparent about limits

    Every system we describe comes with what it cannot do. We publish no invented clients, metrics, or testimonials.

Tell us about the problem.

Send a short, nonconfidential summary of the problem. We read every enquiry and reply to arrange a conversation.

Discuss your project

Prefer email? Write to contact@travelyqorai.com. Sending an enquiry starts a conversation; it does not commit either side to a project.