AI Pillar
LLM Integration Services
Add LLM features without turning your product into a fragile demo—ship with safe actions, measurable quality, and a scalable integration layer.
Problems
What’s slowing teams down
Common bottlenecks we see before AI workflows are implemented.
LLM features ship without boundaries
Without contracts, permissions, and fallbacks, assistants behave unpredictably and become risky to maintain.
Latency and cost surprises
LLM UX feels slow without streaming and routing; costs spike without caching and measurement.
No evaluation baseline
Teams can’t prove improvement without test sets and regression checks tied to real user intents.
Integration debt grows
Hard-coded prompts and glue code make iteration dangerous and slow as the product evolves.
Delivery
What we deliver
Implementation-ready modules designed for reliability, safety, and real operations.
Structured LLM integration layer
A clean module for routing, tools, and outputs—designed to evolve without rewrites.
Tool calling with permissions
Allowlisted tools, role boundaries, and approvals for actions that affect users or data.
Grounding via RAG
Doc-grounded answers with retrieval tuning, plus safe fallbacks when evidence is weak.
Evals + observability
Traces, KPIs, and regression tests to keep quality stable as you iterate.
Deliverables
What you’ll get
Concrete outputs designed for predictable handoff and measurable improvements.
LLM integration layer (routing, prompts, tools)
UX patterns (streaming, states, fallbacks)
Tool schemas + permission boundaries
Optional RAG grounding + retrieval tuning
Evals + regression checks
Handoff docs + runbook notes
Process
How we work
A pilot-first approach, with the quality and governance needed for production rollouts.
Scope
Define workflow, outputs, and KPIs.
Integrate
Implement LLM calls, tools, and UX.
Harden
Add guardrails, evals, and monitoring.
Launch
Rollout plan and documentation.
Stack
Suggested implementation stack
A practical stack we can adapt to your constraints and existing systems.
Automations
Example automations
A few workflows that usually deliver ROI quickly.
In-app assistant with tool actions and escalation
Admin copilot for dashboards with RBAC
Document Q&A with citations and safe fallback
Cost optimization with caching and routing
Start Small
Start small in 7 days
Three pilot-friendly options that reduce risk and ship value fast. Choose one, share access, and we deliver a production-ready baseline.
Standard
AI delivery standard
Quality and safety practices we ship with AI builds so the system stays measurable, maintainable, and production-ready.
Logging + tracing
Conversation and tool traces with request IDs, error visibility, and debug-friendly runbooks.
Guardrails + safety
Tool allowlists, PII-safe patterns, refusal behavior, and escalation routes for edge cases.
Evals + regression tests
Golden queries, scorecards, and regression checks so quality improves over time instead of drifting.
Cost + latency controls
Caching, prompt discipline, retrieval tuning, and routing so your app stays fast and predictable at scale.
Documentation + handoff
Architecture notes, environment setup, and next-step roadmap so your team can iterate safely after launch.
Security-first integration
Secrets isolation, role-based access, audit-friendly actions, and minimal data retention by design.
Pricing
Typical pricing ranges
We confirm scope before starting. These ranges help you plan a pilot versus a full rollout.
Single feature integration: $900–$3,500
Multi-feature LLM module: $3,500–$10,000
Enterprise governance: scoped after discovery
Timelines
Delivery timelines
Common timelines for pilots and production hardening, depending on integrations and governance.
Single feature: 1–2 weeks
Multi-feature module: 2–4 weeks
Risks
Risks & mitigation
The failure modes we design for so reliability and trust stay high.
Latency and user confusion
We use streaming, clear action states, and UI fallbacks so users always understand what’s happening.
Cost spikes
We add routing, caching, and dashboards so spend stays predictable as usage grows.
Unsafe outputs or actions
We enforce policy rules, allowlisted tools, and approvals for high-risk actions.
AI Case Examples
Micro case studies (anonymous)
A few safe examples of outcomes we build for real operations—no client names, just results.
Admin Copilot for Internal Ops
Problem: Operators needed faster answers and safer actions across internal dashboards.
Solution: Tool calling with RBAC boundaries and approval steps for risky operations.
Outcome: Faster ops workflows with predictable and auditable behavior.
LLM Cost Optimization for Production
Problem: LLM usage costs grew quickly with unclear visibility.
Solution: Caching, routing, and prompt discipline plus monitoring dashboards.
Outcome: Lower cost per request and clearer operational control.
Relevant Gigs
Start with a fixed-scope gig
Pick a gig to launch a pilot quickly with clear deliverables and timeline.
Compare
Decision guides
Quick comparisons to help you choose the right approach before building.
Related Services
Explore deeper implementations
When you need more depth than a pilot, these services cover full delivery.
Explore
More AI pages
Additional pillars and use cases to help you plan your roadmap.
FAQ
Frequently asked questions
Can you integrate LLMs into an existing app?
Yes. We add an integration layer that fits your architecture and keeps routing/tools/outputs maintainable.
Do you support streaming responses?
Yes. Streaming improves perceived latency and user understanding. We also design clear action states and fallbacks.
How do you keep costs predictable?
We add routing, caching, and monitoring dashboards so you can track and control cost as usage scales.
Can the model call our internal APIs?
Yes—via allowlisted tools with strict schemas and permission boundaries, plus approvals for risky actions.
Will we be locked into a provider?
No. We can design a provider-agnostic layer so you can switch models or run a hybrid strategy.
Do you include documentation and handoff?
Yes. We deliver source code, setup notes, and next-step recommendations.
Want an AI pilot for your workflow?
Start with a fixed-scope gig or request a tailored implementation plan for your systems.