Starting something new
You are shaping an assistant, copilot, agent, or generation product and need the interaction, context, actions, and safeguards designed as one system.
Service 02
LLM Applications & Agents
Build assistants and agentic systems that can use the right context, take bounded actions, and show when human judgment is required.
Where the work can begin
Built for a first bet and the difficult second act.
Strengthening what already exists
You have a chatbot or automation that demos well but lacks reliable grounding, evaluations, permissions, recovery, or human control.
When this helps
The problems in front of the work.
- 01A generic chatbot cannot answer from business context.
- 02Automation crosses systems without clear control points.
- 03Model quality is being judged by demos instead of repeatable evaluations.
Inside the engagement
The workstreams behind the outcome.
Context and retrieval
Design how the system finds, cites, refreshes, and limits the business knowledge used in each response.
Tools and orchestration
Connect models to APIs and workflows through bounded actions, typed inputs, and recoverable state.
Evaluation systems
Create representative test sets and measurable checks for quality, grounding, safety, latency, and cost.
Human control
Make uncertainty, approvals, overrides, audit trails, and escalation visible in the product experience.
What leaves the room
Tangible progress, not a disappearing workshop.
We design the workflow around evidence, permissions, and recoverable actions. Model choice follows the product requirements rather than defining them.
Representative outcomes
- A model and retrieval architecture chosen for the actual workflow
- Bounded tools and actions with explicit permission and confirmation rules
- Repeatable evaluations for quality, refusal behavior, latency, and cost
- Human-review and recovery paths for consequential or uncertain work
Core deliverables
- Retrieval and knowledge architecture
- Agent tools and orchestration
- Evaluation suites
- Guardrails and human-review flows
Working sequence
Each step earns the next one.
- 01
Model the job
Define what the system may know, decide, recommend, draft, or do.
- 02
Build the grounded slice
Connect the minimum context and tools required for a representative end-to-end task.
- 03
Evaluate failure
Test adversarial, ambiguous, stale, and incomplete cases before widening capability.
- 04
Operate deliberately
Add monitoring, review queues, cost controls, fallback behavior, and clear ownership.
Operating boundaries
What we will not blur.
- We do not describe probabilistic model output as guaranteed truth.
- Consequential actions remain confirmation-gated until their authority and recovery model are proven.
- Provider access, external account approval, and organization policy are tracked separately from application code.
Evidence in the work
Related case studies.
Creator tools · AI workspace
bopGPT
An AI creator workspace spanning model setup, guided prompt creation, content libraries, publishing workflows, moderation, and production operations.
- Generative AI
- Creator Platform
- Operations
Federal procurement · Decision intelligence
Pursuit Intelligence
An evidence-led bid decision platform connecting public opportunities, solicitation findings, pricing scenarios, supplier review, risk, and human-approved pursuit decisions.
- Decision Intelligence
- Evidence Systems
- Procurement UX
Local service · Web + AI
Lloyd's Tire & Auto
A modern local-service platform with a clearer customer journey, local search foundations, a guarded AI shop assistant, and a request-based drop-off experience.
- Conversational AI
- Local SEO
- Service UX
Questions about this service
Fit before scope.
Do you build agents or only chat interfaces?
Both. The interface follows the job. Some systems answer and explain; others use tools across a bounded workflow with approvals and recovery.
Can you work with our existing model provider?
Yes. We can preserve an existing provider when it fits, or compare alternatives against quality, latency, cost, privacy, and operating requirements.
How do you know an AI feature is ready?
Readiness comes from representative evaluations, visible failure behavior, security and permission checks, operating controls, and acceptance in the real workflow—not a polished demo alone.
Start a conversation
Need this capability inside a real product?
Tell us what you are trying to change, who it affects, and where the work stands today. We will give you an honest read on fit and the most useful next step.
Start a project