Enterprise AI deployment, not AI consulting
Enterprise AI agents that hold up in production.
Most agents demo well and fail quietly. We embed Forward Deployed Engineers with your team to build agents into your real workflows — and the evaluation systems that prove they work before they touch anything that matters.
Production AI. Enterprise systems. Measurable outcomes.
The deployment gap
The model is rarely the hardest part.
Most enterprises already have access to powerful models. What stands between a promising pilot and a production system is everything around the model:
- Data integration
- Permissions and identity
- Evaluation methodology
- Workflow redesign
- Reliability under load
- Human oversight
- Security review
- Change management
We close that gap directly — with engineers who build the integration, evaluation, and reliability layer alongside your team, not a slide deck describing it.
What we deploy
Production systems, not demos.
Every deployment is scoped to a real workflow with a measurable outcome — not a general-purpose chatbot.
AI Agents
Multi-step agents that use internal tools and data to complete real tasks, with defined guardrails and human checkpoints.
Research & Knowledge Systems
Search and synthesis over internal documents, wikis, and data — grounded, cited, and scoped to what a role is allowed to see.
Customer Operations
Draft and triage support responses, summarize cases, and route escalations, with agents reviewed before they touch customers.
Finance & Back Office Automation
Reconciliation, invoice processing, and reporting workflows that extract, validate, and route structured data.
Sales & Revenue Workflows
Account research, call summarization, and CRM enrichment that shorten the path from lead to qualified opportunity.
Engineering Productivity
Code migration, test generation, and internal developer tooling that reduce time spent on repetitive engineering work.
Decision Support
Structured analysis and recommendations for operators and analysts — built to inform a human decision, not replace it.
Document & Data Workflows
Extraction, classification, and validation across contracts, claims, and forms, integrated with the systems of record.
Anatomy of a deployment
What we actually build.
Three illustrative examples of how a deployment is structured — what the system reads, what it does, where a human stays in the loop, and the metric it answers to.
A claims team handles thousands of first notices per month. Adjusters read documents and rekey fields before any real judgment work starts.
Inputs
- Claim documents
- Customer correspondence
- Policy data
AI system
- Extract structured fields from submitted documents
- Summarize the claim into a consistent format
- Identify missing or inconsistent information
- Recommend next actions against policy terms
Human
- The adjuster reviews the summary and recommendation
- Low-confidence extractions route to a review queue
- Coverage and payment decisions remain with the adjuster
Measured against
- Time per claim
- Resolution time
- Error rate
- Rework rate
Illustrative examples of deployment structure — not customer case studies, and not reported results.
Agent reliability
How do you know it works?
This is the question that stalls most enterprise agent projects — and the part almost nobody builds. Evaluation is not a report we hand you at the end; it is infrastructure we build alongside the agent from week one.
Eval sets from your cases, not benchmarks
Public benchmarks tell you nothing about your claims queue. We build graded task sets from your real historical cases, with your experts defining what a correct outcome looks like.
A failure taxonomy, not a single score
One accuracy number hides everything useful. We classify how an agent fails — wrong tool, bad retrieval, unsupported claim, silent truncation — because each has a different fix.
Regression testing on every change
Prompts, tools, and models all drift. Every change runs against the eval suite before it ships, so an improvement in one workflow can't quietly break another.
Offline evaluation, then online monitoring
Evals gate the deploy. Traces, sampling, and quality monitoring watch what happens after — including the cases the eval set never anticipated.
Human review where it counts
Sampled human grading calibrates the automated judges. Without it, an LLM-as-judge score is just a number that agrees with itself.
Model changes without re-litigating trust
When a better or cheaper model ships, the eval suite tells you in a day whether you can switch. That is the difference between model-agnostic and model-stuck.
An agent you cannot measure is an agent you cannot safely expand. Every deployment we ship leaves you with the eval harness as well as the system — so your team can keep changing it after we are gone.
How it works
A small team, working software, in weeks.
Identify
Find the highest-value workflow — one with clear economic impact and a measurable outcome.
Embed
Our Forward Deployed Engineers work directly with your team, in your systems, from week one.
Build
Integrate models, enterprise data, internal tools, and business logic into a working system.
Evaluate
Measure reliability, quality, and business impact before expanding scope or scale.
Scale
Turn a successful deployment into a production system and reusable infrastructure.
Why Forward Deployed Engineering
Built differently from traditional consulting.
Both models can produce a strategy. Only one is built to leave you with a running system.
| Traditional AI consulting | Seatrial | |
|---|---|---|
| Team structure | Large project teams | Small senior engineering teams |
| Path to value | Long discovery cycles | Working software in weeks |
| Primary deliverable | Slide decks and roadmaps | Production deployments |
| Reuse | Bespoke, one-off implementations | Reusable infrastructure |
| Pricing model | Billable hours | Scoped to outcomes |
| Feedback loop | Weak product feedback loop | Deployment informs the product |
Every deployment makes the next one faster.
Consulting engagements end when the invoice does. Ours are built to compound: the connectors, evaluation harnesses, and orchestration we build for one workflow become infrastructure the next deployment starts from. You get a system, not a project — and the second workflow costs less than the first.
How we work
Commitments you can hold us to.
Every firm says it values honesty and long-term partnership. These are the versions of that you can actually check — and catch us breaking.
We tell you when the answer isn't AI
Some workflows are better fixed with a script, a process change, or nothing at all. We say so before you pay us to build an agent — even when it costs us the engagement.
Scoped to an outcome, not to hours
We agree the metric before work starts. If the system doesn't move it, that is our problem to solve, not a reason to bill more hours.
You own everything we build
Code, evaluation suites, prompts, and documentation are yours. No proprietary runtime you have to keep paying us to operate, and no lock-in disguised as a platform.
We hand over the reasoning, not just the repo
Every engagement ends with your team able to run, evaluate, and extend the system without us. If you still need us a year later, it should be because you chose to.
The people who scope it are the people who build it
Small senior teams. Nobody is sold to you as an expert and then replaced by someone learning on your budget.
Quality is judged in month six
A demo that impresses in week two is easy. We optimise for whether the system still holds up — and still gets used — long after the launch.
Platform
Model-agnostic by design.
We are not committed to one model provider. Every deployment routes requests to the right model for the task — and can move as the landscape changes.
Routing decisions weigh:
Industries
Built for regulated, complex enterprises.
Financial Services
- · Investment and credit research summarization
- · Compliance and policy review workflows
- · Middle- and back-office operations
Insurance
- · Claims intake and document summarization
- · Underwriting assistance and risk-factor extraction
- · Policy document review
Healthcare
- · Clinical documentation and note summarization support
- · Prior authorization and claims administrative workflows
- · Care coordination and scheduling logistics
Technology
- · Internal developer tooling and code-assistance workflows
- · Customer support triage and response drafting
- · Product and internal knowledge search
Industrial
- · Technical documentation and manual search for field teams
- · Maintenance work-order summarization and triage
- · Quality and inspection report analysis
Logistics
- · Shipment exception handling and customer communication drafting
- · Freight document processing (BOLs, invoices, customs paperwork)
- · Carrier and route research support
Outcomes
Deploy against a business metric.
Every engagement starts by agreeing on the metric that defines success. These are the categories we deploy against most often — not results, since every deployment is different.
Getting started
Start with one workflow.
The best AI transformations do not begin with an enterprise-wide AI strategy deck. They begin with one valuable, measurable workflow.
Engagements are scoped to that workflow and its metric — a small senior team against a defined outcome, not an open-ended hourly retainer.
Illustrative timeline — actual duration depends on scope.
Week 1
Workflow selection and technical discovery
Weeks 2–4
Prototype and integrations
Weeks 4–8
Evaluation, productionization, and rollout
Trust
Designed for enterprise environments.
Architecture can be designed around your security and compliance requirements.
Identity & permissions
Every integration inherits your existing access model — no shadow admin accounts.
Auditability
Every model call and agent action is logged and traceable to a source.
Data boundaries
Deployments respect existing data classification and residency requirements.
Human approval
Consequential actions route through a defined human checkpoint by default.
Deployment controls
Rollout is staged and reversible — nothing goes live without a review gate.
Model flexibility
No lock-in to a single model provider or vendor roadmap.
We work inside your existing controls rather than around them — your identity provider, your data boundaries, your review process. Where a deployment needs to run entirely within your environment, it can. Security review is part of scoping, not something deferred until after a pilot has already touched production data.