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Redian Software
Services

AI strategy that turns into shipped software

Turn AI ambition into a shipped quarterly roadmap — use-case discovery, data and MLOps readiness, ROI and risk assessment for BFSI and enterprise teams.

CMMI Level 3 Appraised ISO Certified 200+ enterprises 5 regional hubs 9+ years of delivery
Outcomes that show up in production

The numbers we move.

Real benchmarks from 3 dimensions our clients measure us against.

  • 60%

    Of AI projects fail

    Our framework filters those out upfront

  • 3–5×

    Realistic ROI

    On classical ML and decisioning use-cases

  • 0

    Reseller margins

    Independent vendor recommendations

What we deliver

Everything in the box.

Comprehensive scope — designed to remove the gaps where most engagements typically slip.

  • 01

    Use-case discovery & prioritisation

    Interviews with your business, data and risk leaders. Ranked candidate use-cases by value, feasibility and time-to-impact.

  • 02

    Data & MLOps readiness audit

    What data exists, what's missing, governance gaps, MLOps stack requirements. Honest gap analysis with remediation plan.

  • 03

    ROI, risk & regulatory plan

    Honest ROI projections — not vendor decks. Bias and explainability assessments. Regulator-aware controls for BFSI and healthcare.

  • 04

    Model & platform selection

    GenAI vs classical ML, open vs closed models, vector store and orchestration choices — independent recommendations, scored against your requirements.

  • 05

    Delivery plan & roadmap

    Quarter-by-quarter execution plan with budget envelopes, owners and exit criteria. Handed over to your team or built by ours.

  • 06

    Governance framework

    Bias testing, explainability (SHAP, LIME), audit trails, regulator-aware controls for EU AI Act, RBI/IRDAI AI guidelines and GDPR.

Who hires us

Built for the way your team buys.

We've shaped this practice around the patterns we see most — match yours against the list.

  • BFSI exploring AI

    Banks and insurers facing AI strategy questions — fraud, pricing, claims, document intelligence — and needing an independent read.

  • Enterprises with AI initiatives

    Large enterprises with multiple competing AI POCs — needing prioritisation and a single coherent strategy.

  • Scale-ups raising on AI

    Series B–C companies with an AI product positioning — needing a credible technical strategy for the next investor narrative.

  • Pre-AI organisations

    Companies new to AI/ML, needing a no-BS map of where to start, what to fund and what to avoid this year.

  • Regulated industries

    Healthcare, BFSI, government — where AI governance and compliance can't be Phase 2 work.

  • Data-rich, AI-poor

    Organisations sitting on rich data with no clear AI roadmap. We help them turn that data asset into shipped products.

Our process

How an engagement unfolds.

Transparent, milestone-driven, with clear owners and timeframes at every stage.

  1. 01

    Scoping call

    What decision are you trying to make? What's already been tried? Who needs to be aligned? We send a written engagement plan within 48 hours.

  2. 02

    Discovery interviews

    Business, data, risk and engineering leaders. Existing-state audit. Current use-cases and pilots reviewed. Output: long-list of candidate opportunities.

  3. 03

    Prioritisation & ROI

    Use-cases ranked by value, feasibility and time-to-impact. Honest ROI projections per shortlisted use-case. Vendor/platform shortlist.

  4. 04

    Readiness audit

    Data quality, governance, MLOps gaps. Hiring profile for in-house roles needed. Regulatory and risk control map.

  5. 05

    Roadmap & handover

    12-month roadmap with budget envelopes, owners and exit criteria. Board-ready presentation. Optional fractional AI architect retainer during execution.

Service overview

In depth — how this practice runs.

The long-form view of what we build, how we sequence it, and the stacks we run.

Enterprise AI decisions fail slowly and expensively when they are made from vendor decks. Redian Software runs AI/ML consulting as a small, senior engagement — CMMI Level 3 Appraised, ISO 27001 and 9001 certified, backed by nine years and 200+ enterprises of production BFSI, insurance and enterprise-operations delivery. We tell you what to build, what to buy, and what to leave alone — with a working prototype, not a slide pack.

What we deliver

  • Opportunity assessment — a structured scan of the business, ranked by expected value, feasibility and regulator posture.
  • Data readiness review — the state of your data (governance, quality, access, provenance), and the shortest credible path to production ML.
  • Build vs buy vs hybrid recommendation — self-hosted, API-based, or fine-tuned model, with total cost, risk and dependency mapped out.
  • Reference architecture — model serving, MLOps, evaluation, retraining, and integration patterns aligned to your existing platforms.
  • Regulatory and model-risk framework — explainability, bias testing, human-in-the-loop policy, audit trail and documentation standards for RBI, SEBI, IRDAI, FCA, CBK, NAICOM and equivalents.
  • Working prototype — one high-value use case taken to a defensible evaluation on your real data, not a marketing demo.
  • Roadmap and business case — sequenced, budgeted and stress-tested with your finance function.

Who we build this for

  • Bank and insurance CIOs and CDOs deciding where to place AI investment across the next two years
  • Boards and audit committees wanting an independent read on model risk and vendor lock-in
  • Product teams evaluating a GenAI feature bet against a real cost-benefit profile
  • COOs weighing document AI and workflow automation against BPO renewal
  • Firms with a stalled AI programme who need a rescue plan grounded in engineering reality

Our approach

Discovery — leadership interviews, workflow shadowing, data and system inventory, regulator scan. Ideation and scoring — a shortlist of opportunities scored on value, feasibility, risk and adoption cost. Prototype — a working evaluation on real data (in a secure enclave) for the top-ranked use case, with independent benchmarks. Roadmap — sequenced two-year plan with build, buy or hybrid calls, integration architecture and named risks. Handover and mobilisation — either handed to your team to execute, or mobilised as a Redian AI/ML development programme. All commitments captured in writing at the end of Discovery. Phased rollouts almost always beat big-bang.

Why Redian for AI/ML consulting

  • Senior engineers only — heads of AI, principal data scientists and domain-embedded architects lead the work.
  • CMMI Level 3 Appraised, ISO 27001 and 9001 — governed process with artefacts that survive audit.
  • BFSI and insurance depth — nine years of production ML in RBI, SEBI, IRDAI, CBK, CBUAE, FCA and NAICOM-supervised environments.
  • Model-neutral — we recommend the tooling that fits, not the vendor with the biggest quota.
  • Multi-region delivery — Noida, Nairobi, Dubai, London and New York.
  • Roadmap to run — you can execute the plan with your team or move it into Redian delivery without a re-bid.

Where we have delivered

AI/ML consulting engagements across India, Kenya, UAE, UK, USA, Canada and Australia, with focused programmes in Nigeria, Tanzania, Uganda, Ghana, Rwanda, Ethiopia, South Africa and Saudi Arabia. Reference deliveries include pricing ML for an African motor insurance aggregator.

Working with Redian

Tell us where you think AI belongs in your business and where you are unsure. A senior consultant responds inside one business day with a written first read and a scoping proposal. NDAs signed on request before the first detailed conversation. Start at /contact, and see our AI/ML expertise for the technical footprint behind the advice.

Why Redian

What makes us different.

Independent reasons clients pick us over Big-4 firms, boutique agencies and offshore vendors.

  • Independent advisory

    We have zero reseller margin on OpenAI, Anthropic, AWS Bedrock or any vendor. Our recommendations earn from delivery, not from picking the vendor that pays us most.

  • We build what we recommend

    Our [AI/ML Development](/services/ai-ml-development) team picks up from the plan — or we hand over to your in-house team with no lock-in.

  • Regulated-industry experience

    Bias testing, explainability, audit trails, regulator-aware controls — delivered in BFSI environments where these are non-negotiable.

  • Honest about failure

    We filter use-cases that won't reach production before you spend on build. About 40% of our consulting ends with 'don't fund this'.

Tech & tools

The stack we ship on.

We pick tools that fit the problem — not because they pay us margin.

  • OpenAI GPT-4o
  • Anthropic Claude
  • Google Gemini
  • AWS Bedrock
  • Azure OpenAI
  • Llama
  • Mistral
  • LangChain
  • LlamaIndex
  • PyTorch
  • TensorFlow
  • scikit-learn
  • MLflow
  • SageMaker
  • Vertex AI
  • Pinecone
  • Weaviate
  • pgvector
  • Snowflake
  • Databricks
  • EU AI Act
  • RBI/IRDAI AI guidelines
  • GDPR
  • SHAP
  • LIME
Proof from production

A case study that mirrors your use-case.

Real customer · real numbers · real go-live. Most of our work is under NDA — this is one we can share publicly.

BankingCanada (Toronto)

SuiteCRM with KYC Automation for a Canada-based Investment Bank

Client · Toronto-headquartered investment bank

  • −55%

    Onboarding time

  • 100%

    Digital KYC documentation

  • Audit-ready

    Regulator compliance

SuiteCRM with integrated KYC automation and DocuSign-backed digital signatures — cutting customer onboarding time 55% for a Toronto-based investment bank.

Tech stack

SuiteCRMDocuSignPrivate Cloud Infrastructure
Frequently asked questions

Everything you wanted to ask before the first call.

Don't see your question? Ask us directly →

What's the difference between AI consulting and AI development?

Consulting is strategy, planning and selection — before any code. Development is the build. Most clients start with a strategy sprint to align on use-cases, ROI and MLOps readiness, then either we build it or they build it in-house with our plan.

How long is a typical AI strategy engagement?

The shape depends on scope — a strategy sprint, a full readiness audit (data + MLOps + governance) and an optional fractional AI architect retainer during execution. Duration is scoped to your actual portfolio and agreed in writing before we start. We end at a written plan with budget envelopes and exit criteria — not an open-ended advisory contract.

What's a realistic ROI for AI projects?

Honest answer: 40–60% of AI projects fail to reach production, often because the use-case was wrong from the start. Our prioritisation framework filters those out before you spend on build. Surviving use-cases typically deliver 3–5× ROI on classical ML (pricing, fraud) and 2–3× on GenAI (agents, copilots) within 12–18 months.

Do you recommend specific vendors or stay agnostic?

Independent. We have no reseller margin on OpenAI, Anthropic, AWS Bedrock, Azure OpenAI, LangChain, Pinecone or any vendor. Our recommendations are scored against your actual requirements — sometimes that's a managed service, sometimes open-source, sometimes a hybrid.

What if our data isn't ready for AI?

It usually isn't — that's why we do data readiness audits before recommending models. Most clients need meaningful data work (cleaning, labelling, governance) before serious ML; the size of that programme is scoped in the audit and agreed with you before we start. We map that work and either deliver it ourselves or hand it to your in-house data team.

Do you do AI proofs-of-concept (POCs)?

Yes — but with a clear bar to production. We design POCs against a measurable business outcome with go/no-go criteria upfront. If the POC clears the bar, we hand over a production architecture plan. If it doesn't, we tell you so before you spend on build.

Can you advise on AI governance and compliance?

Yes. Bias testing, explainability (SHAP, LIME), audit trails, regulator-aware controls (EU AI Act, RBI/IRDAI AI guidelines, GDPR). We've delivered AI in regulated BFSI environments where this is non-negotiable.

Still figuring it out? Tell us what you're trying to solve and we'll send a tailored proposal within one business day.

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