AI, placed where value is

Pragmatic AI.
Real value. No hype.

Dilr.ai builds AI products and runs AI consulting for enterprises ready to capture measurable EBIT impact — not pilots. We place AI exactly where the P&L moves, ship it into the systems your team already runs, then graduate it back to you.

Book a call See productsOpen Voice platform

Products

Threeproducts.One operating layer.

  • ● LiveP/01

    Dilr Voice

    Multi-agent voice AI platform — chain greeter, qualifier, knowledge, and action agents into a single call. Outbound campaigns, inbound front desk, post-call automation.

    • 5+ specialized agents
    • 30+ languages · 40+ countries
    • RAG knowledge base
    • Visual flow builder
    Learn more Open platform
  • ● Coming soonP/02

    Dilr Academy

    AI tutors that diagnose what a learner doesn't understand, then build the shortest path to mastery.

    • Diagnostic-first
    • Socratic by default
    • Parent & teacher dashboards
    • Offline-first
    Learn more Contact sales
  • ● LiveP/03

    DILR Studio

    Promptless content generation, live at dilrstudio.com. Brief once; the studio picks the model, format, and constraints.

    • Brand-voice lock
    • Multi-model routing
    • Approval workflows
    • Per-brief cost ceilings
    Learn more Open Studio

How we work · DATS in six steps

From your systems to compounding value.

We don't replace your stack. We place AI where it pays — shipped into the systems your team already runs, then graduated back to you.

  1. Step 01

    We map your systems.

    CRM, ERP, ticketing, knowledge, telephony, billing. Where decisions are made, where money is made, where time is lost. No AI yet — just the operating reality, in a single one-page system map.

  2. Step 02

    We find the placements.

    Three to five high-leverage spots where AI can compound — not demo. Each placement scored on value, feasibility, risk, and dependency, with an EBIT band attached. You decide which moves first.

  3. Step 03

    We design the operating model.

    Governance, RACI, evaluation lifecycle, escalation paths. AI that fits inside how your organisation already runs — and is audit-ready from day one. The model your CFO and your CISO can both sign.

  4. Step 04

    We ship the first placement.

    Live inside your existing stack — same CRM, same calendar, same knowledge base. Real users, real metrics, 8–12 weeks to production. No parallel platform, no replatforming bill.

  5. Step 05

    We measure and run it.

    Drift, refusal rate, resolution time, EBIT delta. Every placement reports its own truth on a dashboard your CFO can read alongside engineering. Nothing scales until the numbers say it should.

  6. Step 06

    We graduate it to your team.

    You own the system, the runbook, the dashboards. The capability stays after we leave — and the next placement compounds on top of it. The cost of the second placement is half the first.

Six steps. One outcome: AI that runs inside your business — not next to it — and an in-house team that owns it after we leave.

Start with a placement diagnostic Read our approach

Typical first placement: 8–12 weeks to production · No replatforming · Audit-ready governance

The clock is ticking

Adoption is saturated. Value capture isn't.

88% of enterprises now use AI. Only 6% capture material EBITfrom it. The next two years won't reward whether you adopted — they'll reward how deeply you placed it. First-movers compound. Late-movers spend twice to catch up.

In early 2024, 65% of organisations were using AI.0%

A reasonable signal to act — but most CFOs treated it as discretionary spend.

In 2026, 88% are. Your competitors are already inside the curve.0%

Adoption hit saturation in 24 months. The window for AI as a differentiator is closing — what compounds now is depth of placement, not whether you have it.

Only 33% are scaling AI in production. Just 6% have made it core.0%

The 82-point gap between "trying" and "AI-mature" is where every quarter of EBIT impact is decided. Most companies are still inside it.

2.5× EBIT lift goes to the ones who placed AI in core processes.2.5×

AI high performers report 2.5× more EBIT impact than peers. Each placement makes the next cheaper. The leaders are compounding away from the field.

Source · McKinsey State of AI 2025 · Stanford AI Index 2026

Twelve weeks from now, the gap widens again.The 6% reporting EBIT impact next quarter aren't the ones who adopted first — they're the ones who placed AI deepest in their core processes.

Book a placement call See the diagnostic

The new stack · Already shipping

The new stack is the AI stack.

Frontend, backend, database, cache, CDN — meet the new neighbours. Six layers your competitors are already running in production today. Each quarter you wait is a layer of integration debt you'll buy back at 2× the price.

L1 · Models

Frontier + open weights, routed.

GPT-class, Claude-class, Llama-class — picked per request, with caching, fallback, and per-tenant cost ceilings.

  • GPT-4o
  • Claude 4.5
  • Llama 3.x
  • Whisper
  • Embeddings
  • Multimodal
  • Fine-tunes
L2 · Knowledge

RAG that cites, doesn’t guess.

Hybrid search over vectors and BM25, reranked, with freshness windows and source attribution.

  • Vector DB
  • BM25
  • Reranking
  • Chunking
  • Citations
  • Freshness
L3 · Orchestration

Agents with memory + tool calls.

Plan, route, recover. Streaming responses, sub-agents, ReAct loops, function calling against your APIs.

  • Tool calling
  • Memory
  • Routing
  • Streaming
  • ReAct
  • Sub-agents
L4 · Guardrails

Policy, eval, audit — by default.

Refuse the wrong things. Catch drift. Redact PII. Every action ends up in an audit log a regulator could read.

  • Policy
  • Eval harness
  • Drift
  • PII redaction
  • Audit log
  • Approvals
L5 · Integrations

Reads + writes to your real stack.

Salesforce, HubSpot, Workday, Twilio, calendar, ticketing, billing, knowledge — through the keys you already manage.

  • CRM
  • Calendar
  • Phone
  • SMS
  • Email
  • Webhooks
  • Knowledge
  • Files
  • API
L6 · Observability

Every call, every cent.

Traces, latency, refusal rate, cost per resolution. A dashboard the CFO can read alongside engineering.

  • Traces
  • Cost/req
  • Latency
  • Refusal rate
  • SLA

This is the stack you're going to run anyway.The only question is whether you assemble it in 2026 while your competitors are still testing, or in 2027 while they're reporting EBIT.

Place your stack See the DATS system Try Dilr Voice live

DATS · Three entry points

Pick where to start.

Pick a starting point.

Try the live product. Or book the consultation that places AI where it pays.

Try Voice AI Book a consultation

From the blog

Field notes from the front line.

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