AI INTEGRATION AND CONSULTING

AI integration services, for the systems you already run.

Put AI into your ERP, CRM, support desk, and the workflows between them, without ripping anything out. A senior engineer assesses where AI pays off, plans the roadmap, then integrates and implements it. The Readiness Assessment is the fixed-fee entry point; every step after it is scoped from what the assessment found.

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AI integration at a glance

ENTRY POINT
Fixed-fee assessment
STEPS
Assess, plan, integrate, implement
YOU KEEP
Every deliverable
  • Opportunity map with ROI projections
  • Integration architecture
  • Production integration

Trusted by great companies.

  • Notary.io
  • ElephantCPA
  • Docbraces
  • Groundlight
  • Plannerd
  • GloFlow
  • Asana Rebel
HOW IT WORKS

Assess, plan, integrate, implement.

Four steps, each with a written output, each one optional after the first. You can stop after the assessment and keep the plan.

  1. 01

    Assess

    A senior engineer spends two to three weeks in your workflows and systems: a technical audit of what you run, use-case definition with the people who do the work, and ROI modeling on your real volumes. It ends in a ranked opportunity map with build-vs-buy-vs-wait calls. This is the AI Readiness Assessment, a fixed fee scoped on the intro call.

  2. 02

    Plan the roadmap

    The opportunities that survive the ROI test become a sequenced implementation plan: model and provider selection, target architecture, the data each use case needs and whether you have it, integration points, risks, and a first pilot scoped small enough to reach production rather than stall in a demo.

  3. 03

    Integrate

    We connect the AI layer to the systems it has to read from and write to: your ERP, CRM, ticketing, document stores, and the APIs between them. LLM integration through an API or middleware layer, retrieval over your data, guardrails on what the model is allowed to do, and evaluation harnesses so outputs can be trusted before anyone relies on them.

  4. 04

    Implement and run

    The integration ships to production with monitoring, cost controls, and a feedback loop that turns real usage into the next iteration. Handover to your team, or to a Leanware managed agent or dedicated team if you want us to run it. Every step bills against a milestone you can test.

WHAT YOU GET

Three written deliverables, one per stage.

Each stage ends in something you can hand to your team or to another vendor. Nothing here depends on continuing with us.

Opportunity map with ROI projections

Three to five ranked AI opportunities grounded in your volumes and costs, each with the assumptions behind its projection and a build-vs-buy-vs-wait recommendation.

Integration architecture

Which systems the AI touches, how data moves, which model and provider, what stays inside your perimeter, and what the evaluation and rollback plan is, written before code is committed.

Production integration

The working integration in your systems, with monitoring, evaluation reports, and documentation your team can operate from.

WHAT WE INTEGRATE

The AI use cases, technologies, and industries this line covers.

Integration work is specific: a model, a system it reads from, a system it writes to, and a person who has to trust the output. These are the shapes that come up most, and the technologies and industries behind them.

  • Customer support and internal assistants

    Assistants that answer from your documentation and ticket history, escalate when unsure, and write back to the help desk.

  • Document processing and extraction

    Forms, invoices, contracts, and submissions turned into structured data in the system of record, with a review step where accuracy matters.

  • Sales and marketing automation

    Lead scoring, enrichment, and follow-up drafted from CRM data, with the CRM staying the source of truth.

  • Forecasting and predictive analytics

    Demand, churn, and risk predictions delivered inside the workflow where the decision gets made.

  • Recommendations and personalization

    Ranking and recommendation inside your product or storefront, evaluated against the metric you care about rather than click-through alone.

  • Workflow automation and routing

    Multi-step processes across two or more systems, run by an agent with bounded permissions and approval gates for irreversible actions.

  • Fraud and anomaly detection

    Flags on transactions, claims, or sensor data, tuned to your false-positive tolerance rather than a vendor default.

TECHNOLOGIES
Large language models with retrieval over your data (RAG), natural language processing for classification and extraction, computer vision for images and scanned documents, predictive machine learning for scoring and forecasting, and generative AI for drafting and summarization. Provider-neutral across OpenAI, Anthropic, Google, and open models; the choice follows your data residency and cost constraints.
INDUSTRIES
Published work spans healthcare and dental, financial services and accounting, e-commerce, manufacturing and industrial analytics, education, secure document workflows, and B2B SaaS. The integration patterns carry across; the domain rules come from your team during the assessment.

Read the technical deep-dive: how to integrate AI into an existing application

START HERE

The AI Readiness Assessment is the first step.

Every engagement on this line starts with the assessment. It is paid, fixed-fee, and yours to keep, and its recommendation can be to buy a platform, to wait, or to build. The integration and implementation work that follows is scoped from what it found.

AI Readiness Assessment

An AI readiness assessment run ROI-first: a senior engineer works with your team for two to three weeks, audits the workflows in scope, and delivers a ranked opportunity map with build-vs-buy-vs-wait recommendations. A fixed fee, scoped on the intro call. You keep the plan whether you continue with us or not.

DURATION
2 to 3 weeks
PRICE
Fixed fee, set on the intro call
YOU KEEP
The written plan
See the AI Readiness Assessment
AI STRATEGY CONSULTING

AI strategy consulting: the roadmap that follows an assessment.

AI strategy consulting is the advisory work that decides where AI pays off in a business, what to build, what to buy, and in what order. At Leanware it is the roadmap engagement that follows the Readiness Assessment: prioritized use cases, build-versus-buy calls, sequencing, budget shape, and the data and governance groundwork each one needs. A senior engineer who could implement the plan runs it, it is priced as fixed-fee blocks rather than an open-ended retainer, and it ends in a written plan, not a slide deck. If what you need is ongoing advisory capacity, we will say so on the intro call.

AI roadmap consulting

  • Prioritized use cases with the ROI case behind each one, sequenced over the next two to four quarters.
  • Budget shape per use case: what a pilot costs, what production costs, and what it saves against the recorded baseline.

Build, buy, or wait

  • Model and vendor selection: which provider, which model, what stays inside your perimeter, and what it costs at your volumes.
  • AI advisory services scoped as fixed blocks: a senior engineer on call for build-versus-buy decisions, vendor evaluations, and board questions.

Governance and data readiness

  • Responsible AI groundwork: data handling, access boundaries, evaluation standards, and the approval gates an agent has to respect.
  • What your data supports today, what has to change first, and what to hire or train before the first use case ships.

Cost: fixed-fee blocks scoped on the intro call, after the assessment has fixed the use cases. There is no published rate and no open-ended retainer.

AI strategy consulting or the Readiness Assessment first? The assessment measures where AI pays off on your real volumes; strategy consulting sequences and budgets what it found. Almost every engagement runs them in that order.

AI IMPLEMENTATION SERVICES

AI implementation services: from an approved plan to a system your team runs.

AI implementation services take a use case that has passed the ROI test and put it into daily work: the integration, the rollout to the people who will use it, and the monitoring that keeps it accurate after launch. A pilot answers a question on a sample. An implementation runs in production, connected to your systems, measured against the metric the assessment recorded, and owned by someone on your side. That second part is where pilots usually stall, and it is what an AI implementation company should be judged on.

Build and integrate

  • The production build against the integration architecture: model and provider, retrieval over your data, guardrails, and an evaluation harness the outputs have to pass before anyone relies on them.
  • A staged rollout that starts with one team or one workflow, so problems show up in a week of real use instead of across the company.

Rollout and adoption

  • The new workflow written down with the people who do the work: what the AI handles, what a person still checks, and who to call when an output looks wrong.
  • Training for the people who use it, and runbooks for whoever maintains it.

Run and improve

  • Monitoring of accuracy, cost per call, and drift, reported against the baseline the assessment recorded.
  • Prompt, retrieval, and model updates as your data changes, handed over to your team or run by Leanware if you would rather not staff it.

Cost: each milestone is priced before it starts and billed when you can test what it delivered. The total depends on how many systems the AI touches, which the assessment establishes, so there is no published rate card.

Choosing an AI implementation company? Ask to see a system they shipped that is still running six months later, who owns it after launch, and what happens when the model gets something wrong. If the conversation stays on models and tools, you are being sold a pilot.

IS THIS YOU

Five situations that bring people to this line.

The common thread is a business that already runs on real systems and wants AI inside them, not next to them.

  • Your board asked for an AI plan and you need a real answer.

    You run an established business, your leadership wants to know where AI fits, and you would rather have an engineer's read than a vendor's pitch.

  • A platform pilot worked in the demo and stalled in production.

    A SaaS AI feature or a no-code agent handled the easy cases and broke on the real ones, usually where it had to touch a second system.

  • Your data lives in five systems and AI needs all of them.

    ERP, CRM, ticketing, spreadsheets, a document store. The value is in the workflow between them, and no single tool sees the whole thing.

  • You were quoted a strategy engagement priced like a merger.

    You want the plan, not eight weeks of associates and a partner readout. You want to know what to build, what to buy, and what to leave alone.

  • The proof of concept worked and production did not.

    A model that scored well on a sample fails on live data, drifts, or costs more per call than the workflow saves. The fix is usually evaluation, data plumbing, and guardrails, not a different model.

WHAT THIS IS NOT

Not a deck. Not a free audit. Not a reseller.

Not a strategy deck.

Every engagement ends in a written, sequenced plan with owners and integration points, produced by the engineer who could build it.

Not a free audit that sells a build.

The Readiness Assessment is paid, fixed-fee, and yours to keep. Its recommendation can be to buy a platform, or to wait.

Not a tool reseller.

We are vendor-neutral on models and platforms. The recommendation follows your data, your systems, and the ROI math, not a partner margin.

Not a rip-and-replace.

The integration wraps the systems you already run. If a workflow genuinely needs a new system, the plan says so, and why.

CLIENT VOICE

From clients who started with an assessment.

AI Grading Backend for Dental Education Tool

I presented this to my clinic and the response was strong. Leanware did excellent work, and we're going to use feedback from real evaluations to keep refining the AI agent, improving grading accuracy organically as it sees more cases.

EM

Evan Menke

University of Colorado

United States

Custom Software Dev for Cloud-based Solutions Provider

Leanware is transparent, and they’re very honest with the work that they do.

CM

Christopher Massood

Co-Founder & CEO, Elephant CPA

Wayne, New Jersey Clutch verified

See all client reviews

FAQ

Questions we hear on the intro call.

Answered plainly so you can decide whether the assessment is the right first step before you book the call.

  • What are AI integration services?
    The work of connecting AI models to the systems and workflows a business already runs, so they can read from and write to real data: your CRM, ERP, support desk, documents, and the APIs between them. It covers model and provider selection, retrieval over your data, evaluation so outputs can be trusted, deployment, and monitoring. At Leanware the same senior engineer assesses, integrates, and implements.
  • How is AI integration different from AI implementation services?
    Integration is the connecting work: getting a model to operate inside your existing systems and data. Implementation is the broader rollout: the integration plus the process changes, training, monitoring, and ownership that make it stick. Our engagements cover both; the roadmap says where one ends and the other begins for your case.
  • What does an AI implementation company do?
    It takes AI from an approved use case to a system running inside your business. That covers the integration with the tools you already use, the rollout to the people who will rely on it, and the monitoring and updates after launch. A strategy firm usually stops at the plan and a software vendor at the license; the implementation company is accountable for everything in between. At Leanware, the senior engineer who scoped the work is the one who builds it.
  • How do you choose an AI implementation company?
    Judge them on systems in production, not demos. Ask for an example that has been running for six months or more and how it performed after launch, how they check your data before picking a model, what you can test before each payment, and who maintains the system afterwards. Be wary of guaranteed accuracy figures, a tool recommendation before anyone has looked at your data, and no plan for monitoring. Our guide to choosing an AI implementation partner has the full list of questions. How to choose an AI implementation partner
  • What does AI integration consulting cost?
    The Readiness Assessment is a fixed fee, scoped on the intro call by company size and the number of workflows in scope, and billed at signing. Integration and implementation work is scoped from the assessment and billed against milestones you can test, so you see the full cost of each step before it starts. We do not publish a rate card because the range depends on how many systems the AI has to touch; the assessment exists to answer that before you commit to a build.
  • Can you integrate AI into our existing application without a rebuild?
    In most cases, yes. The integration wraps the systems you have: an API layer, retrieval over your data, and an evaluation harness in front of the model. Where a system genuinely blocks the work, the roadmap says so and quantifies the change.
  • Do you do LLM integration, or only your own agents?
    Both. LLM integration into your product or internal tools is a core part of this line: provider and model selection, prompt and retrieval design, evaluation, cost controls, and fallbacks. Custom AI agents are one shape the integration can take when a whole workflow should run on its own; they are a separate line with a managed-service price.
  • Is this AI strategy consulting?
    Yes, in the shape described in the AI strategy consulting section above: where AI pays off, what to build, what to buy, what to wait on, sequenced and budgeted. The difference from a strategy firm is who does it and how it is priced. A senior engineer who could build the recommendation runs it, the work is fixed-fee blocks over weeks rather than an open-ended retainer, and it ends in a written plan. If you need ongoing advisory capacity, we will say so.
  • What does AI strategy consulting cost?
    Fixed-fee blocks, scoped on the intro call once the Readiness Assessment has fixed the use cases. We do not publish a rate because the block size depends on how many use cases survive the ROI test and how much governance and data groundwork each one needs. Strategy firms price the same work as multi-week engagements with a partner readout; ours is priced per written deliverable.
  • Do you offer AI roadmap consulting on its own?
    Yes, when an assessment or an equivalent baseline already exists. The roadmap block produces the prioritized use cases, the sequencing over the next two to four quarters, and the budget shape per use case. Without a measured baseline the roadmap is a guess, so if you do not have one we start with the assessment.
  • AI strategy consultant or AI readiness assessment: which comes first?
    The assessment. It measures where AI pays off on your real volumes and costs; strategy consulting sequences and budgets what the assessment found. Running strategy first produces a plan built on estimates, which is the failure mode most AI roadmaps share. Almost every engagement on this line runs them in that order.
  • How long does an AI integration take?
    The assessment runs two to three weeks. A first production integration typically follows in milestones of a few weeks each, sized to the number of systems involved; the roadmap sets the sequence and each milestone is priced before it starts. We do not quote a total before the assessment because that number is what the assessment produces.
  • What do we need to have ready before starting?
    Access to the people who run the workflows in scope and read access to the systems they use. You do not need a data warehouse, a labeled dataset, or an AI team. Part of the assessment is establishing what your data actually supports.
  • What does an AI integration consultant actually do?
    They decide where a model fits in a workflow you already run, what it reads from and writes to, and how you will know it is working: the assessment, the integration architecture, and the evaluation plan. At Leanware the consultant is the senior engineer who then builds it, so the plan is written by someone who has to live with it.
  • How is AI consulting different from traditional IT consulting?
    IT consulting usually specifies and procures systems; the deliverable is a recommendation and a vendor. AI consulting has to prove the model works on your data before anything is bought, because a model that scores well in a demo can fail on production inputs. That is why our engagements start with a paid assessment on your real volumes and end in a plan the engineer who wrote it can execute.
  • What does an AI consultant cost?
    It depends on the engagement shape (hourly, project-based, retainer, or value-based), on seniority, and on region. Our own work is fixed-fee per stage rather than hourly, starting with the Readiness Assessment, so you know the cost of each step before it starts. For market rates and what drives them, see our guide to what an AI consultant costs. How much does an AI consultant cost?
  • What are the biggest risks in generative AI integration?
    Three come up in almost every engagement: sending data to a model that should not leave your perimeter, outputs that read confidently and are wrong, and a pilot that works on a sample and fails at production volume or cost. The integration architecture handles the first, evaluation harnesses and guardrails handle the second, and scoping the pilot against real volumes handles the third.
  • How do you measure the ROI of an AI integration?
    Against the baseline the assessment recorded: hours per case, cost per transaction, error or rework rate, cycle time, conversion, or retention, depending on the workflow. Each opportunity in the roadmap names its metric and its projected movement before it is built, and the monitoring that ships with the integration reports the actual number.
  • What are examples of AI use in business today?
    The ones we integrate most: support assistants that answer from documentation and write back to the help desk, document intake that turns forms and submissions into structured records, lead scoring and follow-up inside the CRM, demand and risk forecasting in the planning workflow, and agents that run multi-step processes across systems with approval gates. The use cases section above lists them with the technologies behind each. How to integrate AI into an existing application
TRACK RECORD
READY TO TALK

Start with the assessment. Decide the rest with the plan in hand.

A 30-minute discovery call with a senior engineer. We walk through the systems you run and where AI might pay off, and leave you with a concrete next step, whether that is an assessment or nothing yet.

Tell us which systems you run and where you think AI might pay off. A senior engineer will review it and come back with a concrete next step, whether that is an assessment or nothing yet.

Get in touch Send Us a Message See the Readiness Assessment