Eval-Driven Conversational AI Engineering

Prompt Engineering and Conversational AI Services
Design, Test, and Ship AI Conversations Users Can Trust

Devlyn helps teams turn LLM prompts, chat flows, voice flows, copilots, and assistant ideas into production conversational AI systems. We design prompt architecture, conversation policy, structured output contracts, eval datasets, fallback behavior, human handoff, safety controls, observability, and release workflows so your AI assistant is useful beyond the demo.

Scope our conversational AI system See delivery scope

Prompt evals

Golden, adversarial, regression

Structured outputs

Schemas, tools, validation

Conversation design

Fallback, handoff, trust

Conversational AI fails when prompts are treated as copy instead of product logic

The hard part is not writing one impressive prompt. The hard part is making the system behave consistently across real user goals, missing context, ambiguous instructions, channel constraints, tool calls, escalations, refusals, and model changes.

What breaks

The assistant works in a curated demo, then drifts in production because prompts are not versioned, tested, reviewed, or tied to release gates.

Free-form model responses feed downstream code, CRM fields, support workflows, or ticketing systems without schemas, validation, retries, or clear failure handling.

Users lose trust because the AI cannot recover from misunderstanding, explain limits, escalate to a human, remember important context, or admit uncertainty.

Voice and chat journeys use the same prompt even though turn-taking, interruptions, latency, silence, confirmation, and channel-specific constraints require different design.

Model upgrades, provider changes, retrieval changes, and prompt edits happen without eval coverage, so quality regressions are discovered by customers first.

How Devlyn reduces risk

We design prompts as maintained system assets: versioned, documented, tested, reviewed, reusable, and connected to product acceptance criteria.

We create structured output contracts with function calling, JSON schema, validation, repair paths, refusal handling, and downstream integration tests.

We build eval suites around real user intents, edge cases, unsafe requests, tool-routing decisions, tone requirements, recovery paths, and business outcomes.

We design conversation policy for onboarding, clarification, fallback, escalation, handoff, memory, attribution, user correction, confidence, and channel behavior.

We hand over prompt libraries, eval data, test harnesses, release process, observability dashboards, runbooks, and improvement backlog.

What we deliver in prompt engineering and conversational AI

The service is designed for buyers who need a working AI conversation system, not a folder of prompt tips. Scope depends on channel, risk, integrations, data access, and the jobs the assistant must complete.

01

Prompt architecture and prompt library

Define system instructions, task prompts, tool-routing prompts, extraction prompts, response templates, review notes, versioning, ownership, and release criteria.

02

Conversation and dialog policy

Design intent handling, clarification questions, fallback, refusal, escalation, handoff, memory boundaries, confirmations, tone, and channel-specific behavior.

03

Eval datasets and regression testing

Create golden cases, adversarial cases, edge cases, tool-use checks, structured-output checks, safety tests, rubric graders, and release gates.

04

Structured outputs and tool calling

Implement schema-backed responses, function calling, validation, retries, repair strategies, typed API contracts, and downstream workflow integration.

05

Chat, voice, and copilot implementation

Build user-facing chat, in-app copilots, support assistants, voice agents, Slack or Teams assistants, internal knowledge helpers, and workflow copilots.

06

Observability, safety, and handover

Add conversation traces, prompt/version metadata, user feedback, quality dashboards, safety review notes, runbooks, governance guidance, and team onboarding.

The system layers behind reliable conversations

A production assistant needs more than a prompt. It needs contracts, context, memory rules, tests, and operational visibility that match the real job users expect it to perform.

Instruction hierarchy

Separate system, developer, user, retrieval, tool, and response instructions so the assistant knows what must be stable and what can vary by context.

Context and retrieval grounding

Define what context is available, how it is retrieved, how sources are cited, what to do when context is missing, and when the system should ask for clarification.

Tool and workflow contracts

Design when tools should be called, what data they require, how outputs are validated, when actions need confirmation, and how failures are surfaced.

Memory and personalization rules

Decide what can be remembered, what must be session-only, what needs explicit user consent, and what should never be stored or reused.

Fallback and recovery behavior

Build graceful paths for misunderstood intent, missing data, refusal, policy limits, tool errors, low confidence, interrupted voice turns, and human escalation.

Eval and release workflow

Connect prompt edits, model changes, retrieval changes, and tool changes to regression tests, review notes, deployment gates, and rollback paths.

Conversational AI use cases we can build

We shape the implementation around the buyer journey, workflow, and risk profile. A support assistant, sales copilot, voice intake agent, and internal operations assistant should not share the same prompt strategy.

Customer support and service assistants

Customer support and service assistants

Answer policy, account, order, troubleshooting, and knowledge questions with citation, escalation, ticket updates, and safe handoff to support teams.

Sales and revenue copilots

Sales and revenue copilots

Qualify leads, summarize conversations, draft follow-ups, populate CRM fields, recommend next steps, and keep reps in control of customer-facing actions.

In-product copilots

In-product copilots

Guide users through product workflows, explain features, generate artifacts, trigger safe actions, and recover when the user goal is unclear.

Voice agents and phone workflows

Voice agents and phone workflows

Handle intake, scheduling, routing, appointment changes, call summaries, interruption behavior, confirmation, silence, and escalation to humans.

Internal knowledge and operations assistants

Internal knowledge and operations assistants

Help employees search policies, draft documents, answer IT or HR questions, route requests, and interact with approved systems under role-based controls.

Data extraction and workflow assistants

Data extraction and workflow assistants

Convert free-form user conversations into structured records, tasks, forms, tickets, quotes, summaries, or workflow payloads your systems can trust.

Prompt operations and eval discipline

Prompt engineering becomes dependable when every change has a testable reason. We build the review and eval machinery so quality is not based on opinion.

Prompt inventory and versioning

Prompt inventory and versioning

Catalog prompts by purpose, owner, model, channel, data source, release status, dependent workflows, and known failure modes.

Golden and adversarial cases

Golden and adversarial cases

Create test cases from real conversations, user goals, edge cases, unsafe requests, ambiguous wording, policy limits, and expected structured outputs.

Rubrics and automated graders

Rubrics and automated graders

Score helpfulness, correctness, source use, tone, policy adherence, tool-routing, completeness, refusal quality, and output schema compliance.

Regression gates

Regression gates

Run evals when prompts, models, retrieval, tools, or product rules change so regressions are caught before release.

Conversation analytics

Conversation analytics

Track drop-offs, escalation reasons, failed intents, unsupported questions, repeated clarifications, correction events, latency, cost, and user feedback.

Improvement backlog

Improvement backlog

Turn eval misses and production traces into prompt edits, retrieval fixes, product changes, training data, and scope decisions.

How the conversational AI engagement runs

We move from user intent and risk to a working assistant with test coverage, integration contracts, observability, and handover.

We identify user jobs, business actions, data sources, channels, compliance constraints, escalation needs, and the outcomes the assistant must reliably support.
Map jobs, channels, and risk
We define scope, tone, clarification, refusal, fallback, handoff, memory, source attribution, action confirmation, and channel-specific behavior.
Design conversation policy
We implement prompt libraries, structured output schemas, tool contracts, response templates, retrieval rules, and integration boundaries.
Build prompt and schema assets
We build golden cases, adversarial cases, red-team scenarios, rubric graders, safety checks, and release gates around the expected conversation behavior.
Create eval and safety coverage
We integrate the assistant into chat, voice, product UI, Slack, Teams, CRM, support desk, or workflow systems with observability and rollback options.
Ship the assistant path
We hand over prompt docs, eval datasets, dashboards, runbooks, owner map, release process, and backlog so your team can improve the system safely.
Handover and improve

Prompt engineering and conversational AI engagement models

Scoped options for teams that need reliable AI conversations instead of one-off prompt experiments.

Audit

Prompt and Conversation Audit

Best when an existing assistant is inconsistent or hard to change

Scoped

after discovery

Prompt inventory

Conversation failure map

Eval starter set

Improvement roadmap

Talk to Sales
Most Popular

Build

Production Conversational AI Build

Best for shipping a chat, voice, copilot, or workflow assistant

Scoped

after discovery

Prompt architecture

Structured outputs

Eval and safety gates

Production handover

Talk to Sales

Operate

Prompt Ops and Conversation Improvement

Best for live assistants that need ongoing eval and prompt changes

Scoped

after discovery

Regression evals

Conversation analytics

Prompt releases

Quality backlog

Talk to Sales

Who this service is for

This service fits teams that already see a clear user workflow for conversational AI and need reliability, safety, and ownership before exposing it to customers or employees.

01

Product teams adding copilots

You need a product assistant that can guide users, complete actions, explain outputs, and recover when the user goal is unclear.

02

Support and service leaders

You need a support assistant that answers from approved knowledge, escalates cleanly, updates systems, and does not create a trust problem.

03

Operations teams automating intake

You need conversations converted into structured tickets, forms, summaries, workflows, or records without brittle free-form parsing.

04

AI teams standardizing prompt work

You need prompt libraries, evals, ownership, release notes, regression gates, and a process for changing prompts without breaking production.

Trust, safety, and user control

Conversational AI earns adoption when users understand what it can do, can correct it, can escalate, and can trust that sensitive actions are controlled.

01

Capability boundaries

Clarify what the assistant can do, what it cannot do, when it needs more context, and when it should refuse or escalate.

02

Human handoff and approvals

Design handoff rules, approval steps, confirmation moments, escalation payloads, and audit trails for sensitive actions.

03

Privacy and memory controls

Define which data can enter prompts, what can be logged, what can be remembered, what must be redacted, and how users can correct context.

04

Model and provider change control

Treat model upgrades and provider changes as release events with eval runs, prompt review, regression notes, and rollback options.

Build the conversational AI path users can rely on

Share the assistant you want to build, the channel, the user workflow, the systems it must touch, and the failure modes you are worried about. We will help you scope the prompt architecture, eval coverage, and implementation path.

Scope our conversational AI system View AI agents service

hello@devlyn.ai

Prompt architecture

Structured outputs

Eval suites

Handover

Frequently Asked Questions

Direct answers for teams comparing prompt engineering services, conversational AI development, chatbot implementation, voice agents, prompt operations, structured outputs, and LLM evals.

They can include prompt architecture, conversation design, structured outputs, tool calling, eval suites, safety review, retrieval rules, chat or voice implementation, observability, documentation, and handover.

No. Production work includes prompts, schemas, tests, conversation policy, integrations, observability, safety controls, user experience, release process, and operational ownership.

Yes. We can audit prompts, conversation traces, fallback behavior, retrieval, tool calls, escalation paths, analytics, and user complaints, then rebuild the weak areas.

Yes. We can design and build voice flows for intake, routing, scheduling, summaries, confirmations, interruptions, silence handling, escalation, and integration with telephony or product systems.

We reduce risk with grounded context, retrieval checks, source attribution, refusal rules, structured outputs, evals, confidence-aware UX, escalation paths, and monitoring. No production LLM system should rely on prompting alone.

Yes. We can use schema-backed outputs, function calling, validation, retries, repair logic, typed contracts, and downstream tests so model responses can safely feed applications and workflows.

Yes. We create golden cases, adversarial cases, edge cases, tool-routing tests, structured-output tests, safety tests, and regression suites based on the assistant job.

Yes. We can design prompts and eval workflows for multiple providers. The implementation should make provider assumptions explicit instead of hiding them in fragile prompts.

We define escalation triggers, handoff payloads, user messaging, owner queues, approval steps, context summaries, and audit trails so the human receives useful context.

Yes. We can connect to CRMs, support desks, internal APIs, calendars, documents, databases, ticketing systems, Slack, Teams, and product workflows using controlled tool contracts.

Prompt changes should run through version control, review, eval runs, release notes, monitoring, and rollback options. We can set up that prompt operations workflow.

Useful inputs include target users, conversation goals, current transcripts, knowledge sources, existing prompts, support tickets, product workflows, integration requirements, risk constraints, and success criteria.

Yes. For product assistants and copilots, we can pair prompt engineering with AI UX design so users get controls, corrections, source visibility, status, and handoff paths.

Handover can include prompt libraries, schemas, eval datasets, rubrics, implementation notes, dashboards, runbooks, release process, backlog, and team training materials.