Chatbot
Helps a person produce an answer
- Waits for a user prompt
- Works mainly inside the conversation
- Leaves the user to move the work forward
Forward Deployed Engineer Services
Put AI to Work Inside Your Business
Shinetech forward deployed engineers work alongside your business and technology teams to identify high-value workflows, build a working AI solution, and connect it to the systems where work actually happens.
AI agent in the workflow
More than a conversation
- 01ObserveEmail, document, image, record, or system event
- 02UnderstandCompany knowledge, relationships, rules, and context
- 03DecideBest next step within defined business boundaries
- 04ActUse tools, call APIs, update systems, or create work
- 05VerifyRequest approval, escalate exceptions, and record the result
Business-firstStart with the work, not the model
Model-agnosticChoose by fit, cost, privacy, and control
System-connectedWork across CRM, ERP, data, and internal tools
Human-controlledUse permissions, approvals, and audit trails
The implementation gap
You tried AI. Your business still runs the same way.
Your team may already use ChatGPT, Claude, Gemini, or another AI tool to draft, summarize, and answer questions. But orders still need to be checked manually. Customer issues still move between systems. Employees still search through documents, copy data, and wait for approvals.
The missing piece is rarely another chatbot. It is the engineering work between the model and the business.
AI agent
Moves a defined workflow forward
- Responds to a goal, event, or business condition
- Retrieves context and uses approved tools
- Takes controlled action or asks a person to approve it
What an FDE does
Work close to the problem. Stay accountable to the outcome.
A forward deployed engineer works with the people doing the work, translates operational friction into a buildable solution, and stays close enough to see whether it works in practice.
Observe real inputs
Emails, documents, images, records, user actions, and system events.
Use trusted knowledge
Enterprise knowledge bases, RAG, vector search, knowledge graphs, and business rules.
Connect to systems
Tool calling, APIs, MCP, workflow orchestration, CRM, ERP, and internal software.
Keep people in control
Permissions, guardrails, human approvals, evaluations, observability, and audit trails.
DiscoverPrioritizeBuildConnectValidateImprove
Illustrative workflows
What could an AI agent take off your team’s plate?
These are practical examples an FDE can explore and validate with your team.
From inquiry to sales-ready opportunity
Research the company, check CRM history, prepare an account brief, draft the response, and create the next sales action for approval.
Resolve issues—not just reply to them
Combine customer history, product knowledge, order data, and policies; recommend the next step, initiate permitted actions, and escalate exceptions.
Turn document checking into an exception workflow
Extract invoice, purchase-order, and receipt data; match records, flag discrepancies, prepare approvals, and update the ERP after review.
Catch exceptions before they become emergencies
Monitor orders, inventory, shipments, supplier updates, or service queues; explain the impact and trigger the appropriate response.
Make company knowledge usable inside the workflow
Search SharePoint, documents, CRM records, policies, and databases with existing permissions, then use that knowledge to complete the next step.
Turn service history into a recommended action
Combine manuals, technician notes, equipment history, images, and operating data to prepare a diagnosis, work order, parts list, or escalation.
Why Shinetech
Business problem first. Model second. Working software always.
Shinetech is an engineering partner, not a model vendor. We are not tied to one platform or paid to drive token consumption; we stay accountable to the workflow and the software required to make it useful.
Business-first engineering
Start with work that is slow, expensive, inconsistent, or difficult to scale—not with a predetermined AI product.
Model-agnostic by design
Choose commercial, small, open-weight, multimodal, and task-specific models based on the job and its constraints.
Software and integration depth
Design the APIs, data flows, permissions, user experience, testing, monitoring, and maintainable code around the model.
Industry and workflow context
Apply years of software delivery experience across the systems and operating realities different industries depend on.
AI-accelerated engineering
Prototype and iterate faster while engineers remain accountable for architecture, security, testing, review, and maintainability.
Model orchestration
The right model for each part of the job
Shinetech works across model families such as OpenAI GPT, Anthropic Claude, Google Gemini, Meta Llama, Qwen, and Kimi. We select and orchestrate models by task quality, privacy, latency, operating cost, and deployment requirements.
OpenAI GPTClaudeGeminiLlamaQwenKimi
KnowledgeEnterprise knowledge base, RAG, embeddings, vector search, knowledge graph
IntelligenceLLMs, small language models, multimodal AI, model routing, traditional ML
ActionTool calling, APIs, webhooks, MCP, workflow and agent orchestration
ControlPermissions, guardrails, human-in-the-loop, evaluations, observability, audit trails
Engagement model
Start with one workflow, not an enterprise-wide AI program.
Use a focused proof to test value and feasibility before committing to a larger implementation. The work expands only when the evidence supports it.
Identify
Map the workflow, people, systems, data, delays, risks, and desired outcome.
Prove
Build a focused proof with representative data and clearly defined acceptance criteria.
Integrate
Connect real systems, permissions, approvals, monitoring, and security controls.
Improve
Evaluate quality, reliability, cost, and user feedback as the workflow evolves.
Focused first proof
A working POC in as little as two weeks
For a well-scoped workflow with representative data, timely system access, and available stakeholders, the first proof can move quickly. A POC validates feasibility and value; it is not the same as a production rollout.
A useful POC should leave you with:
- A mapped workflow and measurable acceptance criteria
- A working prototype using representative business data
- A clear integration, risk, and production-readiness plan
Proof before promises
AI implementation still depends on dependable software delivery.
The FDE service is backed by Shinetech’s company-wide engineering capacity, long-term client relationships, and experience delivering software that businesses continue to depend on.
25 yearsdelivering business software
500+in-house software engineers
75%recurring business
Security and continuity
Build controls into the workflow—not around it later.
Shinetech’s wider delivery practices include NDAs, ISO 27001, Cyber Essentials Plus, access controls, protected data boundaries, code review, and long-term engineering ownership. The exact controls are defined around the sensitivity and risk of each workflow.
FAQ
Practical questions about working with an FDE.
Understand the role, the starting point, the technology choices, and the control boundaries before deciding whether this approach fits your business.
What is a forward deployed engineer?
A forward deployed engineer works close to business users and technology teams to understand a valuable workflow, build the solution, connect it to real systems, validate it with users, and improve it through operational feedback.
How is an FDE different from an AI consultant?
An AI consultant may focus on assessment and recommendations. An FDE stays close to implementation, turning a selected opportunity into working software, integrations, controls, and measurable acceptance criteria.
How is this different from staff augmentation?
Staff augmentation usually adds engineering capacity to a client-managed backlog. An FDE engagement begins with a business workflow and includes problem discovery, solution design, prototyping, integration, and validation with the people doing the work.
Do we need to know our AI use case before contacting Shinetech?
No. You can start with a workflow that consumes too much time, creates repeated errors, or depends on searching and copying information across systems. The first step is determining whether AI is appropriate and where conventional software or process changes are better.
Which AI models and technologies do you work with?
Shinetech works across model families such as OpenAI GPT, Anthropic Claude, Google Gemini, Meta Llama, Qwen, and Kimi, along with smaller, open-weight, multimodal, embedding, and task-specific models. Selection depends on task quality, privacy, latency, cost, and deployment requirements.
Can an AI agent use private company information safely?
It can be designed around approved sources, existing permissions, access controls, protected data boundaries, and audit requirements. The right approach depends on the sensitivity of the workflow and the systems involved.
Will the agent take actions without human approval?
Only where the business rules and risk level allow it. Sensitive, uncertain, or high-impact actions can require human approval, while routine low-risk steps can be automated within defined limits.
What can be delivered in two weeks?
For a well-scoped workflow with representative data and timely system access, a two-week POC can validate the core workflow, demonstrate a working prototype, and define the integration and production path. It is not the same as a production rollout.
What happens after the POC?
If the proof meets the agreed acceptance criteria, the next stage can add production integrations, security controls, monitoring, evaluations, user experience, rollout support, and ongoing improvement.
Get in touch
Ready to build software that fits your business?
Tell us what you need to build, modernize, automate, or augment with AI. We can start with a focused discussion or a no-risk 1-week trial.
“A fantastic company to work with.” After the initial rapid development project, American Shipping Co. kept two Shinetech developers embedded for nearly four years, supporting internal and external tools and new AI initiatives.
CEO, American Shipping Co. - 5-star Google Review