LLM Applications
We build copilots and assistants directly into your product workflows so teams can draft, summarize, and act in context.
Where the operational bottleneck appears
- Many teams treat AI as a separate browser tab. Operators copy context out of core systems, prompt a public model, and copy results back. This adds steps and disconnects outputs from source data.
- Because the model runs outside the workflow, it cannot reliably act on the task's real context: record state, permissions, and downstream write constraints. Teams still spend manual effort validating and reformatting every output.
- The hidden cost is quality inconsistency. Without embedded controls and evaluation, two operators can run the same task and get different outputs with no structured review path.
We build LLM features inside the tools your team already uses: CRM views, support consoles, operations dashboards, and internal admin products. Instead of a generic chat window, each feature is scoped to a specific job such as drafting, summarization, or recommendation with known input and output contracts. The backend assembles structured context from your application database and business rules, then calls the model with bounded prompts to improve consistency and reduce prompt-injection exposure. In the interface, we design for operational reality: users see source context, confidence cues where appropriate, and explicit edit or regenerate controls before anything is committed. We treat failure handling as part of the product, not a fallback path, so low-quality generations can be corrected quickly without breaking flow. Prompting and evaluation are iterated continuously with measurable quality checks, so improvements are tracked as engineering changes rather than subjective judgments.
sequenceDiagram
participant U as "User"
participant UI as "Application UI"
participant B as "Backend API"
participant L as "LLM Provider"
participant D as "Application DB"
U->>UI: Trigger action
UI->>B: Request draft
B->>D: Assemble task context
D-->>B: Context payload
B->>L: Structured prompt
L-->>B: Draft response
B->>B: Validate and post-process
B-->>UI: Editable draft + source context
U->>UI: Edit and approve
UI->>B: Commit final output
B->>D: Write final resultServer-side context assembly keeps prompts consistent and reduces injection risk from untrusted client input. Drafts are always editable before commit so model output supports operators, rather than silently writing records.
Stack selected for this delivery pattern
Example: A property-management back office was spending hours each week writing listing descriptions and client update emails from templates. We embedded a drafting assistant directly into their internal operations tool instead of introducing a standalone chat interface. When an operator starts a task, the feature pulls existing property fields and historical context from the system record, generates a first draft in the team's preferred style, and returns it inline for editing. Operators can adjust tone, regenerate specific sections, and approve the final text without leaving the page. Because the workflow stays in the system of record, audit trails and downstream usage are preserved automatically. The team no longer copies data in and out of external tools, and the output is more consistent because prompts and structure are standardized in the backend. This turned repetitive writing from a context-switching task into an in-product workflow with clear quality control.
Service-specific answers
No — we design the feature to fit a specific job in your existing workflow, not a general chat widget.
Every generated draft is shown as editable before it is committed anywhere, and we build evaluation into the pipeline so quality is measured, not assumed.
We're provider-agnostic — OpenAI, Gemini, Anthropic, or self-hosted open-source models, chosen based on your cost, latency, and data requirements.