Offshore / Onsite
Hire Dedicated Team
Access a dedicated team of AI engineers and designers, on-site or remote, who build and run your agents alongside your team.
Get StartedAI Chatbots & Assistants
Answers from your documents, with the source attached.
An AI chatbot or assistant is only useful if people trust what it says. We build retrieval over your own content, cite every claim back to the document it came from, and refuse to answer when the evidence is not there.
✓Live in 6–10 weeks
✓Every answer cited
✓Permissions respected per user
6-10 wks
To a cited assistant in production
Every claim
Traceable to a source document
Per user
Permissions enforced at retrieval
Yours
Index, prompts, evals and code
Our Clients
Teams That Ship With Us
WHY MOST ASSISTANTS GET SWITCHED OFF
Nobody abandons a bot
because it was slow
They abandon it because it was confidently wrong once, in front of a customer or a colleague. Everything we build is arranged around making that specific failure rare and visible.
FAILURE MODE
WHAT WE DO INSTEAD
01
It answers from the model’s general knowledge rather than your policy, so the reply sounds plausible and contradicts the handbook.
Retrieval first, generation second. If nothing relevant is retrieved, the assistant says it does not know rather than filling the gap.
02
No citation, so a reader cannot check it and stops trusting it after the first surprise.
Every claim links to the paragraph it came from. Users check the source once, find it accurate, and stop needing to.
03
Everyone sees everything, because permissions were bolted on after the index was built.
Access is enforced at retrieval time against your existing groups, so two users asking the same question get different, correct answers.
04
Content goes stale. The index was built once and the documents moved on without it.
Incremental re-indexing on a schedule, with a freshness report so you can see what the assistant is actually reading.
WHAT WE BUILD
What sits behind the chat window
The interface is the easy part. Almost all of the work — and all of the trust — lives in the layers underneath it.
Content pipeline
Your documents, tickets, wiki and product data parsed, chunked and kept current, with a report on what is indexed and what failed to parse.
Retrieval layer
Hybrid keyword and semantic search tuned on your own questions, because generic retrieval settings are the most common cause of wrong answers.
Permission enforcement
Access checked at query time against your existing groups and roles, so the assistant does not surface a document a user could not open.
Grounded generation
Answers assembled only from retrieved passages, with citations, and an explicit refusal path when the evidence is thin.
Evaluation harness
A question set drawn from real user queries, scored on every change, so a prompt tweak cannot quietly degrade accuracy.
Product surface
The web widget, in-app panel or Slack and Teams integration — with streaming, feedback capture and a visible confidence signal.
WHERE IT PAYS OFF
Places an assistant carries its cost
The common thread is a question asked hundreds of times a week, with the answer already written down somewhere nobody can find.
Customer support deflection
Tier-one questions answered from your help centre and policy documents, with a clean handover to a human the moment the answer is not there.
Agent assist
Your support team gets the cited answer in the console while the customer is still on the line, rather than searching three systems.
Internal knowledge
Policy, process and IT questions answered from the handbook, respecting who is allowed to see which document.
Sales enablement
Reps get product, pricing and competitor answers sourced from approved material rather than from memory.
Onboarding
New starters and new customers ask the questions they would otherwise queue for, at whatever hour they are working.
Document Q&A
Contracts, specifications and reports queried directly, with the passage shown so a professional can verify before acting.
HOW WE WORK
From your documents
to a trusted answer
Accuracy is measured on your real questions before we build the interface. If retrieval cannot answer them, no amount of prompt work will fix it, and we would rather know in week two.
01
Week 1
Audit the content
OUTPUTCoverage and gap report
We take fifty real questions from your team and check whether the answers exist in your documents at all. Roughly a fifth of the time they do not, and that is the finding that matters most.
✓Fifty real questions, supplied by you
✓Source systems inventoried and parsed
✓Content gaps listed before any build
02
Week 2
Prove retrieval
OUTPUTAccuracy baseline
A retrieval-only prototype scored on those questions, with no interface and no polish. This number decides whether the project proceeds, and it is far more predictive than any demo.
✓Hybrid retrieval tuned on your queries
✓Accuracy scored per topic, not averaged
✓Go / no-go decision in writing
03
Week 3-6
Build the answer layer
OUTPUTCited assistant on staging
Grounded generation with citations, the refusal path for thin evidence, and permission enforcement checked at query time against your existing groups.
✓Every claim linked to its source passage
✓Explicit refusal rather than invention
✓Per-user access enforced at retrieval
04
Week 6-8
Evaluate and harden
OUTPUTEval suite in the pipeline
The question set becomes a test suite that runs on every change, plus adversarial testing on prompt injection and permission boundaries.
✓Regressions blocked before they ship
✓Prompt-injection and jailbreak testing
✓Permission boundaries deliberately probed
05
Week 8-10
Launch and tune
OUTPUTLive surface and feedback loop
A staged rollout by audience, with feedback capture on every answer. The first month of real questions is the most useful tuning data you will ever get.
✓Staged by audience, not a flag day
✓Thumbs and comments captured per answer
✓Retrieval retuned on real failures
OUR TOOLKIT
What we build assistants on
Running in your cloud account, on components you can swap. The retrieval layer matters more than the model, and both stay yours to change.
Retrieval & indexing
→
Content pipeline
→
Models
→
Evals & safety
→
Surfaces & platform
→
Pg
pgvector
Vectors beside your data
Pinecone
Managed vector search
Elasticsearch
Hybrid keyword and semantic
Qdrant
Self-hosted vector store
Cohere Rerank
Reordering for precision
OUR TOOLKIT
What we build assistants on
Running in your cloud account, on components you can swap. The retrieval layer matters more than the model, and both stay yours to change.
Retrieval & indexing
→
Content pipeline
→
Models
→
Evals & safety
→
Surfaces & platform
→
Unstructured
Document parsing at volume
LlamaIndex
Chunking and ingestion
Airbyte
Connectors to your systems
Tk
Tika / OCR
Scanned and legacy formats
dbt
Transformation and lineage
OUR TOOLKIT
What we build assistants on
Running in your cloud account, on components you can swap. The retrieval layer matters more than the model, and both stay yours to change.
Retrieval & indexing
→
Content pipeline
→
Models
→
Evals & safety
→
Surfaces & platform
→
Anthropic Claude
Grounded long-context answers
OpenAI
General capability and functions
Google Gemini
Multimodal document Q&A
Ll
Llama
Self-hosted for regulated data
Em
Embedding models
Chosen per language and domain
Claude Agent SDK
Assistants that call tools safely
OUR TOOLKIT
What we build assistants on
Running in your cloud account, on components you can swap. The retrieval layer matters more than the model, and both stay yours to change.
Retrieval & indexing
→
Content pipeline
→
Models
→
Evals & safety
→
Surfaces & platform
→
Ragas
Retrieval and faithfulness metrics
Braintrust
Eval suites and scoring
LangSmith
Traces and prompt versioning
Gr
Guardrails
Output validation and redaction
Pr
Presidio
PII detection and masking
OUR TOOLKIT
What we build assistants on
Running in your cloud account, on components you can swap. The retrieval layer matters more than the model, and both stay yours to change.
Retrieval & indexing
→
Content pipeline
→
Models
→
Evals & safety
→
Surfaces & platform
→
Next.js
Web and in-app chat surface
Slack / Teams
Where your team already works
Zendesk / Intercom
Support console integration
Cl
AWS / GCP / Azure
Hosting in your account
Grafana
Latency, cost and quality dashboards
Case Studies
Products We’ve Designed and Engineered
Selected work for retail, banking, telecom and defence teams, from brand identity and mobile apps to data platforms and AI assistants.
Panda
Fewer Steps From Search to Checkout
Industry
RetailGrocery
Product
WebsiteMobile app
Experience
UX researchProduct design
What We Did
Reworked search, categories and checkout in Panda’s live app and website, in Arabic and English.
- Platform
- App & web
- Market
- Saudi Arabia
EDGE Group
One Brand for 35+ Defence Companies
Industry
DefenceAdvanced technology
Product
WebsiteBrand identityPrint
Experience
Branding & web
What We Did
Brand identity, website and print for a group of 12,000 staff, carried onto the IDEX show floor.
- Scope
- Brand, web, print
- Market
- UAE
Mashreq NEO
The Whole Bank on One Screen
Industry
BankingFintech
Product
Power BIAI assistant
Experience
Data & AI
What We Did
Six performance areas in one index for leadership, plus NEO GPT, a private assistant for bank documents.
- Platform
- Power BI, Azure
- Market
- UAE
Brands For Less
One Shopping App for Ten Countries
Industry
RetailE-commerce
Product
WebsiteMobile app
Experience
UX researchProduct design
What We Did
Found where shoppers dropped off, then redesigned sign-up, banners and cart for a retailer with 120+ stores.
- Platform
- App & web
- Markets
- GCC, Levant, Asia, US
Omantel
Five Spreadsheets Into One Dashboard
Industry
Telecom
Product
Power BI
Experience
UX researchData visualisation
What We Did
Incident, SLA and workload reporting in one Power BI suite for Oman’s first telecom operator.
- Platform
- Power BI
- Market
- Oman
Client Testimonials
In Our Clients’ Own Words
Client Success: Panda
Grocery shopping with fewer steps
We reworked Panda's live app and website, from search to checkout, for shoppers across Saudi Arabia.
- One Shopping Journey
- App + Web
- Equal in Both Languages
- AR + EN
- Live Nationwide
- KSA
"Thanks to UIUX Studio’s work, the client successfully launched their websites. The team maintained a professional approach and scheduled frequent in-person meetings to provide suggestions for improving the project and ensuring its timely delivery."
Shreya LadCEO, Nutroponics"The team is well-versed in the UI/UX domain and has designed a fantastic journey to support the site’s growth projection. Communication, timeliness, and quality of work have been very professional."
Mohammed AbdullahZeaara.com"The Studio has been an absolute game changer for us. After working on a few independent projects, they are now also helping our marketing team, proving their value to our business and earning praise from executives."
BrandonProject Manager"The UIUX Studio team consistently delivers exceptional work with dedication. We’ve completed an amazing app design together, and I’m excited to collaborate with them again in the future."
Hani Farajvancouverhousefinders.com"We are really pleased with the work that UIUX Studio team has completed for us in designing a website for a new brand. They completed the designs quickly, took on board changes, and the finished product is very professional."
AnayaHead of Marketing, Freshminds"I’ve worked with UIUX Studio team on two projects. They capture the vision and run with it! They create exceptional design! Couldn’t be happier with the quality work and files for my developers."
Brandon PugmirePresident, Imprint City
Client Results
Reviewed on Upwork, Trustpilot, Clutch, G2 and GoodFirms
Work With Us
Our engagement models
Work with a dedicated AI team, a fixed-scope project or hourly support. Every model gives you the same in-house engineers and designers.
Estimated pricing
Project Based
Get clear, upfront pricing for a defined AI project, such as a readiness review, a first agent or a production pilot.
Get StartedFlexible engagement model
Hourly
Get AI engineers by the hour, perfect for prompt and evaluation reviews, model or vendor advice, or fixes to an existing workflow.
Get StartedWHAT WE REPORT ON
What lands in the monthly review
Conversation counts and session length tell you nothing about whether the thing is working. These do.
Answer accuracy
Sampled answers scored against your own subject-matter judgement, reported per topic rather than as one average.
Citation validity
Whether the cited passage genuinely supports the claim. A correct answer with a wrong source still counts as a failure.
Refusal rate
How often it declined, and whether it should have. Both over-confidence and over-caution are tracked.
Deflection or time saved
Tickets not raised, or minutes saved per query, depending on which one your team already reports.
Coverage gaps
The questions users asked that your content cannot answer — handed to your team as a documentation backlog.
Cost per answered question
Fully loaded, including re-indexing and the human review that escalated cases still need.
INDUSTRY AI
AI built around your industry’s problems
Pick a sector to see the four layers we deploy, from what users see to how the work is checked.
- L4
Experience layer
What your users see
- Storefront search
- Merchandising console
- Campaign studio
- L3
Agent runtime
What the agents do
- Forecasting agent
- Offer generation
- Stock exception handling
- L2
Retrieval & data
The data the AI uses
- Product & basket data
- Catalogue embeddings
- POS + e-comm events
- L1
Governance & evals
How the work is checked
- Price-change approvals
- Content review queue
- Relevance evals
- L4
Experience layer
What your users see
- Advisor copilot
- Case review workspace
- Client portal answers
- L3
Agent runtime
What the agents do
- Document extraction
- Anomaly triage agent
- KYC file assembly
- L2
Retrieval & data
The data the AI uses
- Core banking records
- Policy & regulation corpus
- Transaction streams
- L1
Governance & evals
How the work is checked
- Full decision audit trail
- Four-eyes approval
- Model risk reporting
- L4
Experience layer
What your users see
- Patient intake flow
- Clinician workspace
- Scheduling assistant
- L3
Agent runtime
What the agents do
- Triage agent
- Documentation drafting
- Prior-auth assembly
- L2
Retrieval & data
The data the AI uses
- EHR integration
- Clinical protocol corpus
- Appointment data
- L1
Governance & evals
How the work is checked
- Clinician sign-off gates
- PHI redaction
- Escalation accuracy evals
- L4
Experience layer
What your users see
- Ops control room
- Carrier comms inbox
- Planner dashboard
- L3
Agent runtime
What the agents do
- Exception resolution agent
- Route & load planning
- Claims handling
- L2
Retrieval & data
The data the AI uses
- TMS / WMS integration
- Carrier & rate data
- Telemetry events
- L1
Governance & evals
How the work is checked
- Value-threshold approvals
- Action audit log
- Resolution quality evals
- L4
Experience layer
What your users see
- In-product copilot
- Support console
- Onboarding assistant
- L3
Agent runtime
What the agents do
- Ticket triage agent
- Release note drafting
- Usage anomaly alerts
- L2
Retrieval & data
The data the AI uses
- Product docs & changelog
- Telemetry & event data
- CRM + support history
- L1
Governance & evals
How the work is checked
- Answer citation rules
- Tenant data isolation
- Deflection quality evals
- L4
Experience layer
What your users see
- Plant floor dashboard
- Procurement workspace
- Supplier portal answers
- L3
Agent runtime
What the agents do
- Requisition matching
- Quality inspection
- Maintenance prediction
- L2
Retrieval & data
The data the AI uses
- ERP & MES integration
- Sensor & inspection data
- Supplier catalogues
- L1
Governance & evals
How the work is checked
- Spend approval gates
- Inspection audit trail
- Forecast accuracy evals
- L4
Experience layer
What your users see
- Enquiry assistant
- Portfolio dashboard
- Deal room workspace
- L3
Agent runtime
What the agents do
- Lease abstraction
- Valuation support
- Enquiry qualification
- L2
Retrieval & data
The data the AI uses
- Lease & title documents
- Market comparables
- CRM pipeline data
- L1
Governance & evals
How the work is checked
- Advisor sign-off
- Document access control
- Extraction accuracy evals
- L4
Experience layer
What your users see
- Citizen enquiry portal
- Case worker console
- Learner assistant
- L3
Agent runtime
What the agents do
- Case-file processing
- Eligibility checks
- Content generation
- L2
Retrieval & data
The data the AI uses
- Records & registry data
- Policy & curriculum corpus
- Service request history
- L1
Governance & evals
How the work is checked
- Decision traceability
- Accessibility compliance
- Fairness & bias evals
Solve one real problem first
Prove value on a single workflow, then reuse the foundation for the next.
WHAT SITS AROUND THIS
The rest of the
agentic stack
An assistant is usually the first thing a team ships and rarely the last. These are the services most often scoped before or after it.
AI Strategy & Consulting
Four weeks to decide whether this is the right first build, and what running it costs at real volume.
View service ↗Custom AI Agents
When users need the work done rather than explained — agents that take real actions with approval gates.
View service ↗AI Platform Engineering & MLOps
The retrieval infrastructure, model gateway and evaluation harness this runs on.
View service ↗Smart Process Automation
Document intelligence where the goal is extraction and routing rather than conversation.
View service ↗FAQ
Questions about assistants
Accuracy & trust
Data & permissions
Cost & rollout
How do you stop it making things up?
What accuracy should we expect?
What happens when it does not know?
Can users tell us when it is wrong?
How do you handle prompt injection?
FAQ
Questions about assistants
Accuracy & trust
Data & permissions
Cost & rollout
Will it show people documents they should not see?
Which systems can you index?
Will our data be used to train models?
How do you handle PII?
How current is the index?
FAQ
Questions about assistants
Accuracy & trust
Data & permissions
Cost & rollout
How is this priced?
What does it cost to run?
Where do users reach it?
Can it hand over to a human?
What if our content is not good enough?
RETRIEVAL PROOF
Send us fifty questions
your team keeps answering.
Forty-five minutes with an engineer. We will tell you how many of them your existing content can actually answer, where the gaps are, and whether an assistant is the right fix. No deck, no discovery fee.
Test it on your contentPlanning an AI Product? Let’s Talk.
Book a free consultation with our design, engineering and AI experts. Share your idea and we’ll come back within one business day with clear next steps.
Recognised By
Our Offices
-
Dubai
UAE · Headquarters
Office OFF2-127, SMARK 2 Building, Plot No. 33-0, Ras Al Khor Industrial Second, Dubai, UAE
-
Mohali
India
B-409, Bestech Business Tower, Sector 66, SAS Nagar, Punjab 160062
-
Adelaide
Australia
Tapleys Hill Road, Fulham Gardens, SA 5024