Skip to main content
Redian Software
Services

AI strategy that turns into shipped software

From AI ambition to a quarter-by-quarter delivery plan. Use-case discovery, data & MLOps readiness, ROI and risk planning — before a line of code.

Talk to our team See our work
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 four dimensions our clients measure us against.

  • 2–4 wks

    Strategy sprint

    Use-case discovery and prioritization

  • 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. 01Days 1–3

    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. 02Weeks 1–2

    Discovery interviews

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

  3. 03Weeks 2–4

    Prioritisation & ROI

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

  4. 04Weeks 4–6

    Readiness audit

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

  5. 05Weeks 6–8

    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.

Most AI investments don't fail at the model — they fail at the plan

The model works in the notebook. The pilot impresses the board. Then it dies in production because nobody mapped it to a business outcome, the data wasn't ready, MLOps didn't exist or the regulator wasn't consulted.

Our AI/ML Consulting & Planning practice fixes that before a line of code.

What we do

  • Use-case discovery & prioritization. We interview your business, data and risk leaders, then rank candidate use-cases by value, feasibility and time-to-impact.
  • Data, infrastructure & MLOps readiness. What data exists, what's missing, what's clean enough, what governance gaps you have, what your MLOps stack needs to look like.
  • ROI, risk and regulatory plan. Honest ROI projections (not vendor decks), bias and explainability assessments, regulator-aware controls for BFSI and healthcare.
  • Model and platform selection. GenAI vs classical ML, open vs closed models, vector store and orchestration choices — independent recommendations, not a Microsoft or AWS reseller pitch.
  • Quarter-by-quarter delivery plan. A 12-month plan with budget envelopes, owners and exit criteria — handed over to your team or built by ours.

Where it's worked

  • A Tier-2 bank in East Africa — we replaced "every department wants a chatbot" with a prioritized 6-use-case plan; first two went live in 5 months.
  • A UK insurer — we sized their ML pricing-engine ambition and produced a 9-month MLOps build plan; FCA-aware controls baked in.
  • A US lender — we did vendor due-diligence on three RAG platforms and stopped a $2M wrong choice before contract signature.

Engagement shape

  • 2–4 week strategy sprint for use-case discovery & prioritization.
  • 4–8 week readiness audit for data, MLOps and regulatory gap analysis.
  • Optional retainer as your fractional AI architect during execution.

Then what?

If you want us to build it, our AI / ML Development practice picks up from the plan. If you want to build it in-house, we hand over the plan, the architecture, the vendor shortlist and the hiring profiles — no lock-in.

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
Read the full case study All case studies
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 2–4 week 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?

Strategy sprint: 2–4 weeks. Full readiness audit (data + MLOps + governance): 4–8 weeks. A fractional AI architect retainer during execution: 3–12 months. 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 2–6 months of data work (cleaning, labelling, governance) before serious ML. 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.

Get a free consultation
Engage Redian

Ready to move on AI / ML Consulting & Planning?

Tell us the outcome you want. A senior engineer will recommend a delivery model and engagement shape within one business day.

Get a free consultation All services