LLM copilots & assistants
Role-aware copilots embedded in your SaaS, admin consoles, or support desks—grounded in policy, with streaming UX, citations, and action buttons that call your APIs safely.
We design and deploy AI integration and automation that your teams actually use: copilots on your data, RAG assistants that cite sources, workflow bots that cut cycle time, and LLM features that stay secure, observable, and cost-aware from day one.
Most “AI projects” stall at a demo. We engineer integrations the way we build products: clear contracts between models and your APIs, tracing for every request, human-in-the-loop where risk demands it, and a path to measurable ROI (tickets deflected, hours saved, revenue assisted).
Whether you are exploring your first copilot or scaling assistants across departments, we bring full-stack depth—Python & Node backends, modern web apps, cloud, and MLOps-minded delivery—so AI sits naturally beside the software you already run.
Orchestration · retrieval · tools · policies
From internal copilots to customer-facing assistants and end-to-end automation—pick a lane or combine them; we map dependencies and ship in slices you can adopt.
Role-aware copilots embedded in your SaaS, admin consoles, or support desks—grounded in policy, with streaming UX, citations, and action buttons that call your APIs safely.
Ingest PDFs, Confluence, tickets, and structured records into hybrid retrieval with re-ranking, deduplication, and freshness rules your legal team can sign off on.
Multi-step flows across Slack, email, CRM, and billing—event-driven, idempotent, with retries and observability so ops keeps running when APIs hiccup.
Tenant isolation, PII handling, prompt-injection mitigations, secrets rotation, and model routing (fast vs. smart) to protect margin at scale.
Bounded agents that call approved tools: CRM lookups, ticket creation, SQL over curated views, document generation—each step logged and rate-limited.
Deploy on AWS, Azure, or GCP with CI/CD, canary releases, tracing (OpenTelemetry-friendly), and cost dashboards for tokens and GPU.
Sync Confluence, Drive, tickets, and databases on a schedule—smart chunking, metadata enrichment, embedding jobs, and rollback-safe re-indexing when policies or models change.
A pragmatic sequence so stakeholders see value early—while we harden the foundations you will depend on later.
We map data sources, user journeys, risk, and success metrics. You get a written architecture, model options, and a phased backlog—not a vague “AI strategy.”
One real workflow end-to-end: retrieval, tool calls, UI, and logging. Validates latency, quality, and integration paths before broader build-out.
Eval harnesses, red-team checks, content policies, access control, and admin controls—so helpdesk and compliance stay in the loop.
Canary users, feedback loops, dataset updates, and prompt/version management. We tune for accuracy and cost per successful outcome.
Deflect L1/L2 with grounded answers, draft replies in your brand voice, and escalate with full context bundles to humans.
Research briefs, CRM hygiene, meeting prep, and proposal drafts tied to your playbook—always with source links and edit trails.
Document extraction, reconciliation helpers, exception routing, and audit-friendly logs—automation first where rules are clear, AI where judgment helps.
Internal dev assistants on your repos and runbooks, test suggestions, and incident triage—without sending proprietary code to the wrong endpoints.
We treat prompts, embeddings, and logs as first-class assets: versioned, reviewed, and owned by you. Data minimization, retention policies, regional deployment, and access reviews are baked into the backlog—not bolted on after launch.
Need SOC2-minded practices, BAA discussions, or air-gapped experiments? We align engineering choices to your risk profile early so procurement and security are partners, not blockers.
No—we integrate the model that fits your constraints: Azure OpenAI, Anthropic on Bedrock, open weights in your VPC, or multi-provider routing. The interface and guardrails stay consistent for your product team.
Yes. We design ingestion with encryption at rest, access-controlled vector stores, chunking strategies that respect licensing, and optional on-prem or dedicated cloud footprints for regulated workloads.
That is normal. We start with a data readiness sprint: schema mapping, de-duplication, metadata enrichment, and human QA loops before anything user-facing ships at scale.
Discovery workshops, fixed-scope MVPs, and dedicated squads are all available. Estimates tie to measurable milestones—prototype, integration hardening, rollout—so spend maps to outcomes, not open-ended “AI research.”