LEX-AI-M / AI THAT CAN DO THE WORK

From daily work
to production AI.

We help your people use AI tools and skills, connect agents to real workflows, and build the secure infrastructure to run it all.

01 / IN PRACTICE

Start with a task.
Show the result.

AI earns a place in the business when a person can point to work that became faster, clearer or easier to review. These are the kinds of workflows we design.

01 / SALES & CUSTOMER TEAMS

Walk into the meeting prepared.

A skill assembles a customer brief from authorized Salesforce records and approved knowledge. An agent drafts the follow-up and suggests CRM updates. The account owner reviews before anything is sent or changed.

CRM + KNOWLEDGE → BRIEF → REVIEW → UPDATE
02 / OPERATIONS TEAMS

Move the exception to the right person.

An agent reads an incoming request, checks the relevant procedure, gathers supporting facts and opens a case with a proposed next step. A team member makes the decision and the record keeps the trail.

REQUEST → POLICY → PROPOSAL → DECISION
03 / ENGINEERING & SECURITY

Ship changes with the evidence attached.

AI helps draft infrastructure code and runbooks. Independent review, tests and security checks challenge the change; an owner approves the plan before the deployment pipeline acts and telemetry confirms the result.

DRAFT → CHECKS → APPROVAL → DEPLOY

02 / WHAT WE BUILD

One path from
first use to scale.

The right starting point may be an employee tool, an agent workflow or an existing platform that needs stronger foundations. Each piece should fit the systems and controls around it.

01 / EVERYDAY WORK

Give employees useful AI, with a way to use it well.

Start with the work already on their desks: writing, research, analysis, meetings and finding the right answer. Turn recurring tasks into reusable, role-specific skills and give people practice using them.

  • Approved AI tools and role-based access
  • Reusable skills, prompts and knowledge sources
  • Training, adoption and outcome measures
02 / CONNECTED WORKFLOWS

Let agents work across systems, with clear limits.

Connect an agent to the documents, tickets and business applications it needs. It can retrieve evidence, prepare a response and propose an action; a person approves consequential changes.

  • Retrieval over permissioned company knowledge
  • Tool and API connections to business systems
  • Scoped actions, approvals and audit trails
03 / PRODUCTION PLATFORM

Build the infrastructure behind the experience.

Move beyond a demo with an AI platform that can ingest data, serve models, run agents and connect to existing workloads. Design the network, identity and deployment path for production from the start.

  • Model gateway, RAG and application APIs
  • Cloud workloads, network boundaries, identity and secrets
  • Infrastructure as code and controlled deployments
  • Evaluation, observability, rollback and cost controls
04 / TRUST & CONTROL

Make security and governance part of delivery.

Decide what data AI may use, what each agent may do and who is accountable. Map controls to your privacy, security and compliance obligations; test them before launch and watch them in production.

  • Use-case inventory and risk assessment
  • Data classification, minimization and access control
  • Agent permissions and human approval
  • Security testing, monitoring and audit evidence

ALSO / SALESFORCE END TO END

Keep your CRM ready for the work ahead.

We set up and configure Salesforce, clean and migrate data, connect it to other systems, automate workflows and provide ongoing support. Where it fits, we can connect Salesforce AI and agents to permissioned data, approval steps and an audit trail.

SETUP · CONFIGURATION · MIGRATION · INTEGRATION · SUPPORT

03 / HOW WE DELIVER

Build the useful part. Prove it is safe to run.

The controls follow the work from the first design session through production operation.

01

Map the task and the data

Identify the user, decision, systems, permitted data, baseline effort and a measurable outcome.

02

Build a narrow working version

Create the skill, agent or application around one workflow; connect only the tools and knowledge it needs.

03

Test the boundaries

Evaluate answer quality and failure cases, protect sensitive data, limit agent permissions and require approval at the right step.

04

Run it and learn

Deploy through controlled pipelines. Monitor quality, security, cost and adoption, then improve from actual use.

04 / WHAT COMES NEXT

Agents will do more than answer.

As agents take on multi-step work across applications, the practical questions become: whose identity do they use, which tools may they call, when must a person approve, and how do we reconstruct what happened?

We design for that future now: scoped identities, policy at the tool boundary, repeatable evaluations, visible decisions and a way to stop or roll back an agent.

05 / PEOPLE BEHIND THE WORK

People who can
take it into operation.

Technical delivery, product decisions and day-to-day operations need to work together.

Scroll through the photos to reveal AI-imagined childhood portraits. Scroll back to see the present again.

Tom Shpizel, current portrait

Tom Shpizel

Technical Lead · Senior Data & AI Architect

With 20+ years of engineering experience, Tom modernizes enterprise data foundations and orchestrates multi-agent AI systems. He connects ERP, Snowflake and legacy databases to modern AI layers including RAG, vector indexing and zero-copy federation.

Read Tom’s full background
AI & agentic architectures
Production-grade RAG pipelines, unstructured data indexing and multi-agent workflows.
Data federation & integration
Zero-copy / BYOL, identity resolution, real-time data harmonization and enterprise ELT/ETL.
Executive strategy & field delivery
Turning ambiguous business problems into scalable technical roadmaps, governing high-volume data ecosystems and advising C-suite leaders.
Core stack & tooling
Salesforce Data Cloud, Agentforce, Snowflake, Python, SQL, vector databases, Claude Code, Cursor AI, C#, REST and GraphQL APIs.

Open to connecting on enterprise data transformation, agentic AI frameworks and architectural strategy.

Paul Uchitel, current portrait

Paul Uchitel

Technical Lead

Inna Chastny, current portrait

Inna Chastny

Vice President and Supreme Leader

Inna’s experience includes leading employee technologies for Canadian Business Banking at BMO Financial Group. Her work spans digital strategy, AI, transformation and simplifying how teams work.

Tanya White, current portrait

Tanya White

Executive Product Manager

As a Program Manager, I lead complex, cross-functional initiatives that connect business strategy with technology execution to drive meaningful, measurable outcomes. I align priorities, optimize resources, manage dependencies, and proactively resolve blockers and bottlenecks to enable high-impact delivery.

Read more about Tanya

I focus on delivery excellence—balancing scope, quality, timelines, resources, and budget while navigating ambiguity and complexity. I empower and coach teams to strengthen ways of working, improve collaboration and transparency, continuously improve delivery, and stay focused on business and technology value.

Passionate about building high-performing, empowered teams and creating the clarity, alignment, and leadership needed to turn strategic priorities into successful outcomes.

Elena Zilberman

Chief Operating Officer

Dmitri Sandler, current portrait

Dmitri Sandler

Chief Executive Officer

A Professional Engineer and PMP with an MBA and an MSc in electrical and computer engineering. Dmitri founded DLSA Consulting and has led work in industrial automation, technology leadership and chip design.

More about Dmitri

06 / START WITH ONE WORKFLOW

What work should
AI help with first?

Tell us who does the task, which systems they use and where the process gets stuck. We can start there, whether the answer is a skill, an agent, infrastructure work or Salesforce support.

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