AI Implementation

Working AI, embedded in your business.

We build AI agents, predictive models, and integrations inside your systems. They run under your governance. Your team owns them when we leave.

Talk to an AI architect See the strategy plan
WORKFLOW · LEAD-RESEARCH-AGENT.YAML LIVE
AGENT
Lead Research Agent
TOOLS
Salesforce · Apollo · Gmail · Calendar
PERMISSIONS
read-only · approval-required for write
LAST RUN · 04:11 UTC · 142 LEADS QUALIFIED · 0 ESCALATIONS
100%
On-budget delivery
100%
Senior-led engineering
5 / 5
Average CSAT
90%
Repeat-engagement rate
What we build with

AI is bigger than chatbots.

Most companies think AI means one category: generative chat. The highest-returning programs use four, deliberately combined.

  1. Agents glyph

    Agents

    Autonomous systems that take actions on your behalf. They research leads, update CRM records, and draft emails. They use MCP servers to talk to your existing tools.

  2. Predictive models glyph

    Predictive Models

    Forecast outcomes from historical data: which leads close, when inventory dips, who's about to churn, how long projects will run.

  3. Analytical AI glyph

    Analytical AI

    Surface patterns humans miss: anomalies in transactions, customer segments, operational bottlenecks, sentiment across communications.

  4. Generative AI glyph

    Generative AI

    Draft proposals, summarize meetings, generate marketing copy, write code. The category most teams already know.

Capability stack

What we build.

Every build uses four primitives. We assemble them inside your systems. They run under your governance. Your team owns them.

Diagram showing how MCP servers, agents, skills, and integrations compose a build
  1. 01

    The Brain

    Vector databases, connected systems, retrieval.

    Every meaningful artifact your business produces becomes a node in one corpus your custom AI runs against. Calls. Briefs. SOPs. Renewals. Board memos. Models. Every one of them, every quarter.

    INCLUDED
    • Vector index over your documents and data
    • Connected source systems (CRM, drive, tickets, warehouse)
    • Retrieval-augmented generation pipelines
    TYPE · CORPUS
  2. 02

    AI Workflows

    LLM fallback, guardrails, PII removal.

    Production workflows with deterministic test harnesses, model fallback chains, prompt-injection defense, and PII redaction at every boundary.

    INCLUDED
    • Multi-model fallback (Anthropic, OpenAI, open-source)
    • Input and output guardrails
    • PII detection and redaction
    TYPE · WORKFLOW
  3. 03

    Observability

    Langfuse, cost ceilings, latency tracking.

    Token cost, deterministic pass rate, p95 latency, per-workflow execution counts. Every system ships with the dashboard the CFO and the architect both read.

    INCLUDED
    • Langfuse-style trace logging
    • Per-workflow cost ceilings and alerts
    • Latency and pass-rate dashboards
    TYPE · INSTRUMENTATION
  4. 04

    Custom Agents & Models

    Bespoke agents, fine-tunes, local LLM deployments.

    When off-the-shelf isn't right: purpose-built agents, fine-tuned and distilled models, and on-prem or VPC LLM deployments your team owns end-to-end.

    INCLUDED
    • Purpose-built agent specs and tool boundaries
    • Fine-tuned and distilled models
    • Local / VPC LLM deployment
    TYPE · CUSTOM
Security

Security is the foundation, not the afterthought.

Every agent we deploy runs under the same three pillars. We build them into the architecture before the first prompt, not at handover.

Security architecture diagram showing audit, permissions, and prompt-injection defense layers
  1. PILLAR 01

    Auditing & Logging

    Every action logged with full traceability.

    INCLUDED
    • Human-readable audit trails: what the AI did, when, and why
    • Real-time alerts on anomalous behavior
    • Full review capability for compliance and security teams
  2. PILLAR 02

    Strict Permission Boundaries

    Least-privilege by default.

    INCLUDED
    • Agents access only what they explicitly need
    • Write actions require defined approval workflows
    • No agent gets blanket access to systems, file systems, or databases
  3. PILLAR 03

    Prompt Injection Protection

    Agents do not get unrestricted internet access. We vet every source.

    INCLUDED
    • Input sanitization and validation on all data flowing into AI systems
    • Sandboxed execution environments to prevent privilege escalation
    • Allowlisted external data sources only
Engagement

How a build runs.

An embedded team operates inside your business. Not from a vendor inbox. Each step has a defined exit criterion.

Diagram showing the embed → iterate → validate → enable build cadence
  1. 01

    Embed

    Strategy lead, PM, engineers, and change manager placed inside the org. Standups, context, relationships.

  2. 02

    Iterate

    Working sprints into production systems. Each sprint deploys functionality, not demos.

  3. 03

    Validate

    Real users, real data, before scale. Friction, edge cases, and failure modes surfaced early.

  4. 04

    Enable

    Hands-on training in parallel with build. Internal champions equipped before we step back.

Who runs the engagement

Four senior people. Inside your business.

No junior consultants on client work. No subcontracted strategy team. The four people who run discovery are the four people who'll architect the build. The same names through Phase 4.

Strategy lead leading a working session with two team members
Role 01

AI Strategy Lead

Senior architect who runs discovery, designs the solution architecture, and signs the fixed-price scope. The single owner of the Plan.

Owns
Operations Map, Solution Architecture, Scope
Implementation PM running a stand-up
Role 02

Implementation PM

Runs the engagement: interview scheduling, decision gates, deliverable QA, and the daily stand-up that keeps Phase 1 and Phase 2 paired, not sequential.

Owns
Stand-ups, gates, deliverable QA
Engineers reviewing code together on dual monitors
Role 03

AI Engineer

Pressure-tests the architecture against your real systems and data. Validates feasibility against your APIs, your data quality, and your integration constraints. Not against benchmarks.

Owns
Tech assessment, architecture validation
Change manager facilitating a training session
Role 04

Training & Change Manager

Designs the adoption plan in parallel with engineering: role-based training, resistance mapping, internal champion picks. Builds the runway before the build starts.

Owns
Change plan, training curriculum
Same four names through Phase 3 + 4 if you proceed to implementation. No re-staffing.
Technologies we work with

We work with the platforms you already know.

Our senior enterprise architects bring deep expertise across the cloud, data, model, business, and observability platforms your team already uses. We don't ask you to switch tools. We integrate with what you have.

  1. 05 · Observability how teams manage performance and cost
  2. 04 · Business Platforms where the agent acts
  3. 03 · AI Models where reasoning comes from
  4. 02 · Data & Warehousing where structured data lives
  5. 01 · Cloud & Hosting the infrastructure
The entire Elevate team did a wonderful job with the entire implementation. They made it scalable, easy to use, and accommodated all of the needs and asks set forth for the build, including customizations that only those with deep development experience could achieve to make the lives of our users easier.
Skyler Hawkins CRM Lead
Next steps

Two ways to start.

Before we build anything, we make sure it's the right thing to build. Pick the entry point that fits where you are.

Discovery call

Discovery call

Talk through your context. We'll tell you whether AI fits, and which category fits best.

Delivery
DELIVERY · 30 MIN
Pricing
PRICING · NO FEE
AI Operating Plan

AI Operating Plan

Discovery, opportunity ranking, solution architecture, change plan, and engagement scope. Funded, signable, ready to execute.

Delivery
DELIVERY · 6 WEEKS
Pricing
PRICING · FIXED ON SCOPING

Let's start with a conversation.

1270 Avenue of the Americas, New York
We're not here to sell AI for the sake of AI. Discovery call · 30 minutes · senior architect on the line.

Talk to an AI architect See the AI Strategy plan