Services

Find what matches your situation.

Start with the problem that sounds like yours. Each section explains when it applies, what changes, and the exact services underneath.

What brings you here?

01

We need governance and compliance in place

Turn policy into controls people actually follow under deadline pressure.

See details 02

Our teams need to adopt AI safely

Get from pilot enthusiasm to workflows your firm can run every week.

See details 03

We're putting agents into production

Make agentic systems survivable before they touch real users or data.

See details 04

We need to modernize legacy systems

Use AI to accelerate migration without carrying old workflow debt forward.

See details 05

AI costs are getting out of control

Find what is leaking spend across cloud, tooling, and abandoned pilots.

See details 06

We're choosing an AI vendor

Buy the right tool with eyes open on data, security, and fit.

See details 07

Our data isn't ready for AI

Fix the inputs before you blame the model for bad outputs.

See details

01

Your situation

We need governance and compliance in place

Turn policy into controls people actually follow under deadline pressure.

Most firms have an AI policy. Fewer have controls that work when consultants paste client data into a tool at 11pm. We implement governance that fits how your teams really operate.

You might be here if

  • AI usage is spreading faster than legal or security can review it
  • You have a policy document but no clear approval or audit trail
  • DPDP, client confidentiality, or sector rules are starting to apply to AI workflows

What changes

Clear ownership, usable controls, audit-ready documentation, and a governance model your teams can follow without slowing delivery to a halt.

Services in this area

AI governance and compliance operations

  • DPDP and AI governance implementation

    Data boundaries, consent flows, retention rules, and AI-specific controls mapped to your regulatory context.

  • AI readiness audit

    A structured review of current AI usage, leakage risk, policy gaps, and what to fix first.

  • Monthly compliance operations

    Ongoing monitoring, control checks, and governance support so compliance does not reset after the first audit.

02

Your situation

Our teams need to adopt AI safely

Get from pilot enthusiasm to workflows your firm can run every week.

Adoption fails when training is generic and tooling is disconnected from real deliverables. We roll out AI inside the workflows your teams already use.

You might be here if

  • A few power users are ahead while everyone else is unsure what is allowed
  • Pilots work in demos but stall when they hit real client or internal work
  • You need structured training, not another lunch-and-learn on prompting

What changes

Teams that know what to use, how to use it safely, and where AI fits in their actual delivery process.

Services in this area

AI rollout and team enablement

  • AI training and implementation for teams

    Role-based enablement, workflow design, and hands-on rollout inside live team environments.

  • AI implementation for regulated or document-heavy firms

    Deployment patterns for firms where confidentiality, review steps, and document workflows cannot be an afterthought.

03

Your situation

We're putting agents into production

Make agentic systems survivable before they touch real users or data.

Agents look capable in a sandbox until permissions, edge cases, and failure modes show up in production. We test, harden, and prepare your team to own what ships.

You might be here if

  • An agent demo impressed leadership but no one owns reliability or escalation
  • You are unsure what happens when the agent hits bad data, wrong tools, or ambiguous instructions
  • Security review is blocking launch because failure handling was never designed in

What changes

Production-ready agents with tested failure paths, security review support, and a clear incident playbook.

Services in this area

AI agent reliability and security

  • AI agent QA and reliability lab

    Structured testing for edge cases, tool misuse, drift, and breakdown scenarios before go-live.

  • AI agent security

    Permission models, data exposure review, prompt injection hardening, and access control design.

  • AI incident response

    Runbooks, escalation paths, and support when an AI system misbehaves in production.

  • Enterprise AI sandbox

    A controlled environment to test agents, integrations, and policies before wider rollout.

04

Your situation

We need to modernize legacy systems

Use AI to accelerate migration without carrying old workflow debt forward.

Legacy systems slow every AI initiative downstream. We modernize the software and workflows that AI depends on, not just bolt models onto brittle infrastructure.

You might be here if

  • Critical internal tools are old, manual, or held together with workarounds
  • AI pilots keep hitting the same data and workflow bottlenecks
  • You want modernization that reduces operational drag, not another layer on top of legacy debt

What changes

Modernized workflows and systems that give AI cleaner inputs, faster delivery paths, and less operational fragility.

Services in this area

Legacy modernization with AI

  • Legacy software modernization with AI

    Targeted redesign and migration of old systems where AI can accelerate analysis, rewrite, or workflow replacement.

05

Your situation

AI costs are getting out of control

Find what is leaking spend across cloud, tooling, and abandoned pilots.

AI cost problems rarely show up in one line item. They hide across duplicate subscriptions, idle infrastructure, over-provisioned models, and pilots that never shut down.

You might be here if

  • AI and cloud spend is rising without a clear owner or usage map
  • Multiple teams are paying for overlapping tools and model access
  • You suspect spend is going to experiments that should have been killed months ago

What changes

A clear picture of where AI money goes, what to cut, what to consolidate, and how to control spend as usage scales.

Services in this area

AI cost optimization and waste reduction

  • Cloud and AI cost optimization

    Usage review, model routing, infrastructure right-sizing, and ongoing cost control recommendations.

  • AI waste audit

    Identification of redundant tools, zombie pilots, and spend with no measurable business return.

06

Your situation

We're choosing an AI vendor

Buy the right tool with eyes open on data, security, and fit.

Vendor decks optimize for demos, not your operating reality. We evaluate options against how your teams work, what data they touch, and what controls you actually need.

You might be here if

  • Procurement is comparing vendors on features, not data handling or operational fit
  • You need an independent view before signing a multi-year AI platform deal
  • Internal stakeholders disagree on which tool should become the standard

What changes

A structured vendor comparison, risk framing, and a recommendation your security, legal, and delivery teams can align on.

Services in this area

Vendor evaluation and procurement support

  • AI procurement and vendor evaluation desk

    Requirements mapping, vendor shortlisting, security and data review, and procurement decision support.

07

Your situation

Our data isn't ready for AI

Fix the inputs before you blame the model for bad outputs.

AI quality problems often start upstream — fragmented documents, stale knowledge bases, inconsistent metadata, and no clear source of truth. We prepare the material your systems need to work.

You might be here if

  • Retrieval quality is poor because source documents are messy or duplicated
  • Knowledge is spread across drives, wikis, inboxes, and tribal memory
  • You are about to launch an AI product on data nobody has cleaned in years

What changes

Cleaner knowledge assets, better retrieval inputs, and a migration path from scattered information to AI-ready source material.

Services in this area

Data cleanup and knowledge migration

  • AI data cleanup and knowledge migration

    Source review, deduplication, structuring, and migration of internal knowledge into AI-usable formats.

Engagement model

Most work starts as an audit or scoped implementation, then moves to retainer or external execution depending on what you need to run in-house.

Audit · Implementation · Retainer · External execution partner

Discuss your need