Everyone has AI now.
Almost no one has AI that pays off.

Wherever you are with AI — mapping your first workflow or scaling what's already in production — we find where it earns its keep, then build it.
200+
AI/ML systems in production
2X
Faster execution
with AI workflows
160+
Сloud certifications

The Problem

WHERE AI STALLS

Most AI spend buys motion, not movement.

Pilots launch, demos impress — then nothing in the operation changes. Almost always for the same reason: the work was scoped to a tool, not an outcome.

  • Outcome mapped first
    Before any tool is chosen
  • Built only if it moves a number 
    No pilots for their own sake
  •  Data unified to the decision
    Not another dashboard
  • Shipped to production
    Not stuck in proof-of-concept
Map your highest-leverage workflow
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What We Build

AI Adoption Consulting
Stop guessing where AI fits. Get a ranked, build-ready shortlist before anyone writes code.
AI Transformation 
Change how the work gets done — we manage the people and systems, not just the rollout.
Workflow Automation & Agents
Hand whole processes to agents that hold up in production, not just the demo.
AI Product Development
Ship AI features your users trust — tested on real data, enterprise-secure, on time.
Data Foundations
Make AI reliable instead of eventually broken — pipelines, entity resolution, governance underneath.
Applied Research & Prototyping
Find out what won't work fast, before you fund building it.
Get your workflow assessed
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Not sure which applies? Submit your workflow for a fit, feasibility, and build-path recommendation — no sales call needed.

how we work

Three commitments, every engagement.

AI is the default in how we build — and the bar we hold ourselves to before we ask you to trust it.

  • AI is how we work, not what we sell
    Embedded in every build: review, testing, architecture. 2X delivery, no corners cut.
  • You see it before you trust it 
    Bi-weekly demos in a live environment. The real thing, not a status deck.
  • We'll tell you what not to build
    If AI won't help, you'll hear it before you spend.
  • No number we can't source
    Every metric traces to a real engagement.

Real Deployments. Real Outcomes.

Entity-typed RAG + autonomous agents
3,390+
Community members live and queryable on the platform in MVP launch

ASK, DON'T SEARCH

Problem:
An innovation community's value is its relationships — who works with whom, on what, funded by whom. But that lived scattered across profiles, publications, patents, and grants. Keyword and vector-only search flatten exactly the identities and connections that matter.
Approach:
Entity-typed RAG over people, organizations, publications, patents, and grants — retrieval over structured fields with weighted scoring, then LLM reasoning grounded in the matched evidence. Two surfaces sit on top — natural-language Match Search and Community Insights — kept current by three orchestrated autonomous agents.
Outcome:
A community of thousands — people, papers, partnerships, grants — queryable like a colleague, not a database. Members get ranked, reasoned matches and grounded answers that cite the entities behind every claim.
Prophet time-series on retail demand
3,000+
Stores on forecast-driven schedules

FORECAST, DON'T GUESS

Problem:
Rolling averages drove scheduling across thousands of stores. Peak days were understaffed — missed shipments, late orders. Slow days were overstaffed — wasted payroll.
Approach:
Prophet-based time-series forecasting on daily order-picking volumes, integrated directly into the scheduling workflow. Sales signals, store-level seasonality, and live adjustments replace last-quarter averages.
Outcome:
Forecast-driven rosters across 3,000+ stores. Shipment delays down, labor waste down, manager time freed. Scheduling that adapts to real demand instead of last month's.
ML demand forecasting on
part-level data
↑25%
Higher forecast accuracy

PREDICT, DON'T REACT

Problem:
Planners reconciled part-level demand by hand across a sprawling catalog. Forecasts lagged real consumption — stockouts on fast movers, dead stock on slow ones.
Approach:
ML demand forecasting feeding an automated part-level planning pipeline. Consumption signals and supplier lead times replace manual spreadsheet reconciliation.
Outcome:
Forecast accuracy up 25%, planner time on data tasks down 60%, inventory bottlenecks down 20% — with a full audit trail on every number.
Automated triage and routing
↑60%
Faster issue resolution

RESOLVE, DON'T QUEUE

Problem:
Support tickets sat in manual triage. Routing depended on whoever picked them up, and resolution times slipped as volume grew.
Approach:
Automated ticket classification, routing, and lifecycle workflows on the service platform. Tickets reach the right owner the moment they land.
Outcome:
Resolution 60% faster, less manual triage, consistent handling as volume scales.
See more
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Where Do You Start?

Readiness to Roadmap

For enterprises at the start of the adoption curve. We assess where AI fits, where it doesn't, and sequence what's worth building first.
Take the AI Opportunity Audit
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  • Workflow prioritization
    Which processes are ready for AI and which aren't
  • Risk and control model
    Guardrails defined before you ship
  • Phased roadmap
    Sequenced by impact and feasibility
  • First deployable capability
    Targeted within 6–8 weeks after the assessment
Take the Al  Opportunity Audit
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Data to Decisions

For teams with operational data they aren't using. We build the intelligence layer on top — dashboards, forecasting, anomaly detection, semantic search — shipped with vertical-specific patterns.
See industry deployments
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  • KPI dashboards
    Built around how your team actually operates
  • Forecasting & anomaly detection
    Predictive signals on top of your existing data
  • Vertical-specific patterns
    Consistent outputs shaped for your industry
  • See before committing
    Live industry demos, no pitch deck required
See industry deployments
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Start a project

Share a few details—company size, what you’re building, timeline, and budget range. We’ll reply within one business day with next steps and an initial read on fit.
OPEN INTAKE FORM
Takes ~2 minutes. No commitment.
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Talk to us

Skip the form and talk to someone who’s shipped production software and production AI. Tell us what you’re building—we’ll be direct about what’s realistic, what’s risky, and what we’d do first.
Book a call
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