Our AI consulting and audit services give you a clear, engineering-grounded view of where AI fits your business, what it takes to build it right, and which opportunities deliver real returns. Senior practitioners, structured assessments, and a plan you can act on from day one.
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150+
AI Projects Assessed
60+
Engineers In-House
96%
Client Retention
Built for teams at a real decision point.
Make the right call, before you invest in a build.
Most teams either rush into a build before the strategy is clear or spend months on frameworks that stay on slides. Here is the honest comparison across the dimensions that determine whether an AI initiative delivers value.
| What matters |
Recommended
Custom build
|
Common alternative
Off-the-shelf SaaS
|
Other path
Low-code platform
|
|---|---|---|---|
| Use case quality | Ranked by data readiness, effort, and business impact | Defined by stakeholder opinion | Defined by industry benchmarks |
| Data readiness | Assessed before any build begins | Discovered mid-build | Typically out of scope |
| Architecture decisions | Made before the cost is committed | Made under delivery pressure | Rarely part of the engagement |
| Build confidence | A PoC validates feasibility on real data | Confidence assumed | Confidence theoretical |
| Team alignment | Stakeholders aligned on scope and ROI before build | Alignment happens during the build | Alignment often stays at the slide level |
| Accuracy expectations | Benchmarked against your actual documents and data | Set after the first production results | Expressed as aspirational targets |
| Time to value | 2 to 4 weeks for strategy, 3 to 6 for PoC | Months before the signal | Months before any engineering output |
| Long-term ROI | Higher: right use cases, right architecture from the start | Variable: depends on early scope decisions | Often needs further engineering engagement |
From first strategy to a production-readiness review, we cover the full consulting spectrum. Start with the assessment that addresses your most pressing question and build from there.
A prioritised roadmap of where AI delivers the most value, with honest effort estimates and a 90-day plan.
Use-case rankingROI mapping90-day plan
We work with your leadership and engineering teams to map where AI can deliver the most value in your operations, ranked by data readiness, integration complexity, and business impact. You walk away with a prioritised roadmap, honest effort estimates, and a 90-day plan for the first initiative.
A structured view of whether your data, tooling, team, and governance are ready to build AI.
Data auditInfrastructure reviewTeam capability
Before you build, you need to know if your organisation is ready. We assess your data infrastructure, tooling, team capability, and governance posture, giving you a structured view of what is in place, what needs strengthening, and the most direct path to your first production AI system.
A ranked shortlist of the workflows where AI reduces time or improves accuracy, scored on a consistent framework.
Process mappingImpact scoringBuild vs buy
Your operations hold more AI opportunity than most teams realise. We map your workflows, identify the processes where AI can reduce time or improve accuracy, and score each use case against a consistent framework: effort, data availability, compliance constraints, and measurable ROI. The output is a ranked shortlist your team can act on.
An independent technical review of the AI already running in your environment, with a prioritised improvement plan.
Accuracy benchmarkingBias reviewIntegration health
An independent technical review of AI systems already running in your environment. We evaluate model accuracy on current production data, assess bias exposure, review data pipeline integrity, and check integration health, delivering a structured report with a prioritised improvement plan and clear benchmarks for each finding.
A clear view of whether your data and governance are production-ready, with a remediation plan.
Data qualityPipeline reviewGovernance framework
AI performance is determined by data quality long before a model is chosen. We assess your data pipelines for completeness, consistency, and availability, review your governance posture against relevant compliance requirements, and give you a clear remediation plan so your data is production-ready when your AI initiative is.
A 2 to 4-week engagement that proves your use case on real data, with a production recommendation.
Real dataAccuracy benchmarksProduction path
A structured 2 to 4-week engagement that proves the feasibility of your target use case on your actual data and real documents, rather than synthetic benchmarks. We build a working prototype, measure accuracy against agreed targets, surface the architectural decisions that will govern the full build, and deliver a production recommendation your engineering team can plan against.
Every strong AI build starts with the right question.Tell us where you are. We will tell you what to assess first.
What we assess, across every layer.
Different questions, same rigour. Whether you are assessing strategy, systems, or data, you get the same senior consultants, the same structured methodology, and findings your team can act on.
Clarity on where AI creates real value for your business.
A structured review of your operations to identify the workflows where AI can reduce time, improve accuracy, or unlock new capability, ranked by impact and feasibility.
Where your industry peers are using AI, what is working, and what is still early, so your strategy is informed by production reality rather than hype.
An honest assessment of which AI capabilities to build in-house, which to procure, and which to develop with an engineering partner, based on your team's capability, timeline, and total cost of ownership.
A clear picture of what your AI systems are actually doing.
Evaluation of model accuracy on current production data, including drift detection, confidence calibration, and a benchmarked view of where performance can improve.
A structured assessment of algorithmic bias across your model outputs, identifying where automated decisions carry fairness exposure and how to address it before it becomes a compliance or customer issue.
An end-to-end review of the systems your AI models connect to: data pipelines, API contracts, monitoring coverage, and the operational health of everything downstream.
The foundation every AI system depends on.
A structured review of your core data assets: coverage, consistency, labelling quality, and the specific gaps that will affect model training and production performance.
An assessment of your data governance posture against the frameworks that matter for AI, GDPR, DPDP, HIPAA, and sector-specific requirements, with a clear remediation plan.
A review of the pipelines that feed your AI system: ingestion, transformation, storage, and freshness, with a readiness score for each and a prioritised action plan.
Consulting for every vertical.
How we consult, every step of the way.
Structured methodology, senior practitioners, and findings your team can act on. Here is how every engagement runs in practice.
Every engagement starts with a structured discovery session mapping your current workflows, data assets, team capabilities, and business objectives. We come prepared, ask the right questions, and turn the outputs into a clear picture of the landscape before any assessment begins.
Our assessments are built on your actual data, your actual systems, and your actual team's capabilities. Every finding is supported by evidence, and every recommendation is scoped to what your organisation can execute.
Every engagement delivers a structured report with findings ranked by impact and effort, a recommended action sequence, and enough technical detail for your engineering team to act without starting over. No slide decks that go on a shelf.
The consultants who assess your AI landscape are the engineers who can build it. If the engagement surfaces a clear build opportunity, the same team carries it forward, so context stays intact and the transition from advice to execution is measured in days.
Every assessment is grounded in your actual data and systems. Recommendations are scoped to what your team and infrastructure can realistically execute, with timelines your organisation can plan against.
Our consultants are AI engineers. They have shipped production systems, run evals, and debugged models at 3am. When they assess your AI landscape, they are telling you what they would actually build.
Every assessment runs on your actual data and systems. Recommendations are grounded in what we find in your environment: your pipelines, your models, and your team's capabilities.
Findings are ranked by impact and effort. Every recommendation comes with enough technical detail for your engineering team to execute. The deliverable is a plan your team can act on, not a slide deck.
The consultants who assess your opportunity are the engineers who can build it. Same team, same context, so nothing is lost between advice and execution.
Selected work Real outcomes.
Selected AI consulting and audit engagements across financial services, healthcare, logistics, and enterprise operations.
View All Case StudiesOur achievements display our capabilities
Trusted voices. Real outcomes.
“We truly appreciated their dedication, technical expertise, and problem-solving approach.”
Young Onion
Department Head
“I was blown away by the knowledge the team had about creatives, e-commerce, website design, and optimization.”
Decathlon Sports India
Image Leader
“They have a good team of designers and project managers who help us with the designs using HTML, Angular, and React.”
Instarama
COO
“Their creativity stands out. A collaborative team that delivered high-quality solutions working closely with us.”
CodeGama LLP
Business Developer
“We truly appreciated their dedication, technical expertise, and problem-solving approach.”
Young Onion
Department Head
“I was blown away by the knowledge the team had about creatives, e-commerce, website design, and optimization.”
Decathlon Sports India
Image Leader
“They have a good team of designers and project managers who help us with the designs using HTML, Angular, and React.”
Instarama
COO
“Their creativity stands out. A collaborative team that delivered high-quality solutions working closely with us.”
CodeGama LLP
Business Developer
“The product has become more intuitive and user-friendly. Load times dropped significantly after their work.”
Qoruz
Co-Founder
“Simply put, the quality of their code is excellent. They integrated third-party software and ensured GDPR compliance.”
VIA IOM
Director
“What impressed us most was how well they understood our brand and translated it into clean, thoughtful designs.”
GD Farm Fresh
Director
“The product has become more intuitive and user-friendly. Load times dropped significantly after their work.”
Qoruz
Co-Founder
“Simply put, the quality of their code is excellent. They integrated third-party software and ensured GDPR compliance.”
VIA IOM
Director
“What impressed us most was how well they understood our brand and translated it into clean, thoughtful designs.”
GD Farm Fresh
Director
A structured four-phase consulting model, from landscape mapping to an action plan your team can execute against from day one.
Book a call{ 01 }· 1 week
Structured stakeholder interviews, workflow mapping, data inventory, and systems review. You walk away with a documented picture of your current AI landscape, your key constraints, and the specific questions the assessment will answer.
{ 02 }· 1 to 3 weeks
We run the agreed assessment, strategy mapping, model audit, data review, or PoC build, on your actual data and systems. Every finding is evidence-based and every recommendation is scoped to your environment.
{ 03 }· End of engagement
A structured report with findings ranked by impact and effort, a prioritised action sequence, and technical specifications for each recommendation. Presented to your team with the context to act on it immediately.
{ 04 }· Ongoing
If the engagement identifies a clear build opportunity, the same team carries it forward, scoping the build, running the PoC, or embedding as your engineering partner. Assessment to execution in one continuous engagement.
Engagement models, built to fit.
Senior AI consultants engaged from week one. Choose the shape that matches your question, your timeline, and how far you want to take it.
A structured assessment with a defined scope, methodology, and deliverable. Fixed price, a clear timeline, and a findings report your team can act on at handover.
A senior AI consultant embedded in your leadership and engineering conversations, running assessments, validating decisions, and bridging strategy to execution as your AI programme evolves.
It depends on where you are. A strategy engagement maps your AI opportunities, ranks them by impact and feasibility, and gives you a prioritised roadmap with honest effort estimates. A readiness assessment looks at your data, infrastructure, and team capabilities. An audit reviews AI systems already in production. A PoC validates a specific use case on your actual data before a full build is scoped. We scope each engagement to the question that matters most to you right now.
Most strategy consultancies deliver frameworks and slides. We deliver findings grounded in your actual data, systems, and team capabilities, written by engineers who have shipped production AI themselves. When the assessment is done, the same team can carry it forward into building, in one continuous engagement.
We assess your data infrastructure for quality, coverage, and pipeline health, the foundations that determine model performance before a model is chosen. We also review your tooling, team capability, and governance posture. The output is a structured readiness score, a gap analysis, and a remediation plan with a clear sequence for addressing what matters most.
A focused strategy or readiness assessment typically runs 2 to 3 weeks. A full use-case prioritisation engagement runs 2 to 4 weeks. A model or data audit runs 1 to 3 weeks, depending on the complexity of the systems involved. A PoC validation runs 2 to 4 weeks on real data. Timelines are confirmed in scope before any engagement begins.
You do, fully. Every deliverable, findings, reports, roadmaps, PoC code, and architecture recommendations, is yours at handover. We build on open, portable foundations and transfer everything cleanly.
We sign an NDA before any data or system access. Assessments can be structured to work with anonymised samples, synthetic data, or isolated environments depending on your data sensitivity requirements. We architect the assessment around your compliance posture from the first planning session.
The assessment report is structured to be acted on without additional context. If the findings surface a clear build opportunity, the same consulting team can carry it forward, scoping the build, running a PoC, or embedding as your engineering partner. Many clients move from assessment to build with us in a single continuous engagement.
Yes. Independent audits of third-party or in-house AI systems are a core part of our practice. We review model accuracy on current production data, data pipeline integrity, integration health, and governance posture, regardless of who built the original system.
Cost transparency, before the call.
We put numbers on the page. Here is the honest band by engagement type, plus the five variables that move the number once we scope your specific situation.
| Tier comparison | Single question or audit Focused Assessment $8K - $25K ₹6.5L - ₹20L |
Most chosen
Most chosen Full Consulting Engagement $25K - $80K ₹20L - ₹65L | Series A and beyond AI Programme Advisory $80K - $200K ₹65L - ₹1.6Cr | Ongoing capacity Embedded Advisory from $12K / month from ₹10L / month |
|---|---|---|---|---|
| Timeline | 1 to 3 weeks | 3 to 8 weeks | 2 to 6 months | Ongoing monthly |
| What you get |
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A single-system audit is materially simpler than a full organisational readiness assessment spanning multiple data estates and teams.
Clean, accessible data with clear schemas is faster to assess than fragmented, multi-source estates that need significant discovery.
Each AI system or data pipeline in scope adds assessment time, especially where integrations, APIs, or legacy connectors are involved.
Engagements covering HIPAA, RBI, PCI DSS, or GDPR require additional governance review and documentation that adds scope.
A lightweight feasibility test on a single document type is far simpler than a multi-category PoC with accuracy benchmarks across five variants and edge-case coverage.
Bands include consulting time, structured deliverables, and all findings documentation. Any PoC build costs are included in the scoped engagement price.
Tell us the question you are trying to answer: strategy, readiness, audit, or PoC validation. A senior AI consultant replies within one working day. Direct conversation, real answers, a real plan.
Step 1 - Tell us where you are
Which AI question is most pressing right now, and what a useful answer looks like for your team. We sign an NDA before any specifics.
Step 2 - Speak to an AI consultant
A senior AI consultant joins within two working days to map your situation, your constraints, and the right assessment scope for your stage.
Step 3 - Get a real plan
A structured assessment scope, a timeline, and a findings format you can actually use, built for your specific situation.
Tell us the question you are trying to answer: strategy, readiness, audit, or PoC validation. A senior AI consultant replies within one working day. Direct conversation, real answers, a real plan.
