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Enterprise AI services

Enterprise AI implementation and consulting

LockedIn Labs is an enterprise AI implementation and consulting firm. Our forward-deployed engineers work with your operators to define a useful workflow, build and integrate the software, evaluate its behavior, and prepare your team to run it. The engagement connects the business outcome to the code, decisions and acceptance evidence needed for production.
Scope an AI implementation

From an operating problem to accepted work

  1. 01

    Define the outcome

    Name the workflow, baseline and accountable owner.

  2. 02

    Approve the boundary

    Agree data, access, permitted actions and system owners.

  3. 03

    Build and evaluate

    Implement the workflow and test representative cases.

  4. 04

    Accept and transfer

    Review the evidence, authorize release and assign operating ownership.

Acceptance connects the implementation to the people responsible for operating it. The charter records the evidence and decisions needed at each boundary.

01 · The delivery model

Keep the business decision close to the engineering.

Enterprise AI consulting starts with the operating problem: an overloaded review queue, fragmented knowledge, a difficult application boundary, or a process that needs a dependable decision trail. The useful unit of progress is a workflow whose performance, authority, and ownership can be tested.

Forward-deployed engineering (FDE) is how we deliver that work. Engineers embed with the people who understand the operation and carry the context into architecture, software, integration, and acceptance. Product, domain, security, and operations specialists participate where their judgment changes the result.

Execution starts with a named outcome owner, approved data and access, and a baseline the reviewers can use. The engagement follows the evidence. A discovery can establish a viable implementation scope. A pilot can test a specific assumption. A production engagement can carry an approved workflow through integration, release, and transfer. Team composition and commercial commitments follow the agreed work and its dependencies.

02 · The buying decision

How to evaluate an AI implementation firm

Ask each delivery partner to connect the proposed outcome to the systems, evidence, and operating responsibilities behind it. These five questions give your business, engineering, and security owners a shared way to compare an engagement.

Buyer questions and the evidence to request from an AI implementation firm
Buyer questionEvidence to requestWhat an adequate answer identifies
What outcome are we buying?Workflow brief and baselineUsers, current process, acceptance owner and measurable change.
What will connect to our systems?Integration and action contractData sources, access, permitted writes, retries and recovery.
What happens when the AI is wrong or a dependency fails?Representative evaluation cases and recorded resultsFailure conditions, hold/escalation path and responsible operator.
What exactly was accepted for release?Candidate, gate results and release decisionMatching artifact and configuration, outstanding conditions and release authority.
Can our team operate it afterward?Receiving-team exercise and runbookDemonstrated responsibilities, remaining dependencies and support boundaries.

Inspect our reference materials

These reusable examples help you prepare a scope and acceptance discussion with your delivery team.

Explore our synthetic policy exercise to inspect a policy decision and the evidence behind it.

03 · Services

Build the complete path into operation.

AI strategy and architecture

Select a workflow with a measurable operating consequence. We examine the process, available data, technology estate, and decision rights, then define the architecture and investment sequence. The result is a scoped implementation brief: what changes, who accepts it, which dependencies matter, and what evidence supports the next commitment.

Explore AI transformation

AI and agent implementation

Build the product around approved context, appropriate models, constrained tools, and human judgment. Work can include retrieval, document processing, decision support, agent workflows, and custom applications. We develop the evaluation cases alongside the behavior, so every material change can be examined against the task the system is expected to perform.

Explore AI product engineering

Enterprise integration and modernization

Connect AI to the systems that carry the operation: applications, APIs, documents, data stores, and events. We establish identity, permissions, source lineage, write boundaries, and recovery behavior. An integration is accepted through representative transactions, including retries and exceptions, with the existing system owner involved in the review.

Review the enterprise access model

Production assurance and capability transfer

Turn readiness into evidence your reviewers can inspect. We implement evaluation and release gates, control testing, monitoring, escalation, and operating instructions for the agreed scope. Your receiving team practices running and changing the system. The handoff connects code, configuration, ownership, and the decisions that explain how the implementation works.

Inspect production readiness

04 · Regulated operations

Engineer around the consequence of each action.

Healthcare is our deepest practice. Through HealthNext, the work is grounded in payer operations such as prior authorization, claims, member service, and enrollment. Those workflows make source quality, human authority, identity, and a reconstructable record concrete engineering concerns.

The method also applies across industries. A financial operations review, a regulated document process, and a service workflow each need their own domain criteria. We scope the relevant sources, permitted actions, reviewers, and failure consequences with your specialists, then design the system around those decisions.

For example, a document assistant may assemble a source-linked draft while an authorized reviewer owns the consequential action. Acceptance includes the missing-document case, a conflicting source, an unavailable downstream system, and the route back to normal operations. Our production acceptance framework provides a practical worksheet for that review.

05 · What you receive

Make the engagement inspectable from the first decision.

The deliverables connect the operating mandate to the implementation. Agree on the artifacts at scoping, assign an acceptance owner, and keep them current as evidence changes.

Decision record
A workflow charter, architecture decisions, data and authority boundaries, dependencies, and an acceptance plan tied to the business outcome.
Working system
Reviewed source, integration contracts, versioned configuration, representative evaluations, release evidence, and the implementation instructions for your environment.
Operating capability
Monitoring and incident procedures, escalation owners, recovery exercises, training, and a transfer record showing what the receiving team can operate.

06 · Engagement fit

Start with a decision the enterprise is ready to make.

This work fits leaders who can name an operating outcome, bring the relevant system and business owners into the engagement, and create an approved path to representative data and environments. A focused scope gives those people something concrete to evaluate together.

The first conversation establishes the workflow, its current baseline, the users affected, and the next decision. We ask which systems must connect, what information can be processed where, which actions require approval, and who will run the result. Existing work, procurement constraints, and the availability of reviewers shape the sequence.

Budget and timing depend on that scope: integration complexity, data readiness, evaluation depth, access approvals, and the operating commitment after release. We make those assumptions explicit before setting the delivery commitment.

Scope an AI implementation

07 · Learning resources

Build the skills behind the implementation.

Teams preparing for AI delivery need a shared way to discuss the work: frame an operating problem, integrate the right systems, test failure cases, and explain who owns the result. The LockedIn FDE Benchmark learning hub offers practical pathways for developing those skills. The benchmark is an initiative of LockedIn Labs; these educational resources support learning and practice.

Use the AI skills self-assessment to reflect on your starting point and choose the next exercise. Connect that exercise to an artifact a colleague can review, such as a workflow map, evaluation set, or handoff note. Self-reflection guides a learning plan; verified capability depends on the work and evidence reviewed.