AI & Data
Generative AI products, copilots and content systems
From internal copilots to customer-facing generative features, we design GenAI products that stay on-brand, on-policy and measurably useful.
5x
Faster first drafts across content and proposal teams
90%
On-brand output after style tuning and review loops
Weeks
Not quarters, to a usable internal copilot
Region
Locked data residency for India, UAE and KSA
Definition
What genai means here
Generative AI produces new artefacts — text, code, images, structured drafts — conditioned on your data and rules. In an enterprise it needs grounding, brand and policy guardrails, review workflow and evaluation to be safe in production.
Capabilities
What is included
Each capability is scoped, estimated and delivered against agreed acceptance criteria.
Copilot design
Task-focused assistants embedded in the tools your teams already use.
Grounded generation
Retrieval over your documents, policies and product data to keep output factual.
Brand & policy guardrails
Tone, terminology, legal disclaimers and prohibited-claim filters.
Multilingual output
English, Hindi, Gujarati and Arabic with RTL-aware formatting.
Human review workflow
Draft, review, approve and publish with version history.
Evaluation
Rubric-based scoring and A/B tests on real business tasks.
Deliverables
What you receive
- GenAI product definition and success rubric
- Grounding corpus and retrieval pipeline
- Copilot UI embedded in target workflow
- Brand, tone and policy guardrail layer
- Review and approval workflow
- Evaluation report with quality benchmarks
Engagement models
How we can work together
Fixed-scope project
Defined outcome, milestone billing and a signed delivery plan. Best when requirements are stable and the business case is approved.
Dedicated squad
A cross-functional pod (architect, engineers, QA, delivery lead) reserved monthly for a rolling roadmap with sprint-level reporting.
Managed service / AMC
SLA-backed run and evolve model covering monitoring, incident response, security patching and a monthly enhancement allowance.
Implementation roadmap
How the engagement runs
Indicative timeline for a single-entity engagement; multi-country programmes are phased.
- 01
Frame · Week 1
Define the job to be done and the quality rubric that defines 'good'.
- 02
Ground · Week 2-3
Assemble and index the corpus the model must be faithful to.
- 03
Build · Week 4-6
Prompt architecture, UI, guardrails and review workflow.
- 04
Evaluate · Week 7
Blind scoring against the rubric and a human baseline.
- 05
Launch & tune · Ongoing
Roll out, gather feedback signals and improve on a cadence.
Comparison
Generic GenAI tools vs a grounded enterprise copilot
| Factor | Public GenAI tool | Grounded enterprise copilot |
|---|---|---|
| Knows your products and policies | No | Yes |
| Data leaves your boundary | Often | No |
| Consistent brand voice | Unreliable | Enforced |
| Auditable output history | No | Yes |
Advantages
- Large productivity gains on drafting and research tasks
- Grounding keeps output tied to approved source material
- Deployable inside your own cloud and region
- Reuses one retrieval layer across many copilots
Trade-offs to plan for
- Quality depends on the corpus you can supply
- Requires a review workflow for anything customer-facing
- Model costs need monitoring at high volume
Decision guide
Is this the right service for you?
Match your situation to the recommended starting point.
| If this sounds like you | We recommend |
|---|---|
| Teams draft repetitive documents | Internal copilot with templates and grounding. |
| Customers ask product and policy questions | Grounded knowledge assistant with escalation. |
| You need actions, not text | Move to AI agents. |
Industries
Where we apply genai
FAQ
GenAI questions, answered
Related services
Often delivered together
LLM Integration
We embed LLM capability into products you already run — search, summarisation, classification, drafting — with the retrieval, caching and evaluation that keep it accurate and affordable.
AI Solutions
We take AI from boardroom ambition to audited production: use-case discovery, data readiness, model selection, evaluation harnesses and the MLOps needed to keep results stable after go-live.
UI/UX
Research-led design for complex, data-dense enterprise products — with a token-driven design system so what we design is exactly what gets shipped.
Ready to scope your genai engagement?
Share your context and we will come back with an approach, timeline and indicative investment — under NDA.