AI Pillar
AI Development Company
Ship AI systems that teams trust: grounded answers, safe tool actions, and automation with retries and visibility.
Problems
What’s slowing teams down
Common bottlenecks we see before AI workflows are implemented.
Demos don’t survive production
Without tool contracts, reliability patterns, and monitoring, AI features break quickly after handoff.
Accuracy and trust issues
Users won’t adopt assistants that hallucinate or can’t cite sources and policies reliably.
Integrations are brittle
APIs fail and workflows break without retries, idempotency, and structured logging.
No quality loop
Without evals and KPIs, improvements are guesswork and regressions slip into production.
Delivery
What we deliver
Implementation-ready modules designed for reliability, safety, and real operations.
Agents that take actions (safely)
Tool calling with allowlists, approvals, and structured actions so outcomes are predictable and auditable.
Grounded RAG knowledge systems
Ingestion, chunking, metadata, retrieval tuning, and optional reranking for measurable accuracy gains.
Automation that operators trust
Webhook-driven workflows with retries, logs, and clean handoffs—built for real operations.
Evals + observability by default
Test sets, traces, and KPIs so quality is measurable and improves over time.
Deliverables
What you’ll get
Concrete outputs designed for predictable handoff and measurable improvements.
AI feature architecture + integration plan
Working pilot integrated into your product/tools
Guardrails: allowlists, RBAC hooks, approvals
Evaluation set + regression checks
Tracing/logging baseline + runbook notes
Source code + handoff documentation
Process
How we work
A pilot-first approach, with the quality and governance needed for production rollouts.
Discovery
Define workflow, tools, data boundaries, and KPIs.
Design
Choose agent/RAG/automation patterns and contracts.
Build
Implement integrations, UX, and reliability patterns.
Evals
Ship test sets and measurable quality checks.
Launch
Rollout plan + monitoring + handoff.
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.
Support deflection + ticket escalation
Lead qualification + scheduling handoff
Invoice/KYC extraction + review queue
Approval-aware ops routing and syncing
Internal admin copilot with RBAC boundaries
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-workflow pilot: $900–$3,500
RAG MVP (docs → answers): $2,500–$8,000
Agent MVP (tools + approvals): $3,500–$12,000
Production hardening: scoped after discovery
Timelines
Delivery timelines
Common timelines for pilots and production hardening, depending on integrations and governance.
Audit / discovery: 3–7 days
Pilot build: 1–2 weeks
MVP rollout: 2–4 weeks
Risks
Risks & mitigation
The failure modes we design for so reliability and trust stay high.
Unclear success criteria
We define KPIs and eval queries up front so progress is measurable (not subjective).
Unsafe actions or outputs
We enforce tool allowlists, RBAC boundaries, approvals, and safe fallbacks for edge cases.
Quality drift after launch
We ship regression checks and monitoring so the system improves instead of drifting silently.
AI Case Examples
Micro case studies (anonymous)
A few safe examples of outcomes we build for real operations—no client names, just results.
AI Knowledge Base Across 2,000+ Pages
Problem: Teams needed fast answers across long PDFs, but search was slow and inconsistent.
Solution: RAG with retrieval tuning and safe fallbacks for weak evidence.
Outcome: Reliable answers with measurable improvements on real queries.
Ops Automation With Approval Gates
Problem: Manual approvals and tool syncing created delays and fragile processes.
Solution: Event-driven workflows with validation, retries, and operator visibility.
Outcome: Reduced manual load while keeping humans in control of risky actions.
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
Do you work with OpenAI, Claude, and others?
Yes. We pick providers based on accuracy, latency, cost, and your data/security constraints, and can design for provider flexibility.
How do you prevent hallucinations?
We ground answers via RAG where needed, enforce safe fallbacks, and ship eval sets so quality is measurable.
Can agents take actions in our tools?
Yes. We expose allowlisted tools with strict schemas and permission boundaries, and add approvals for risky actions.
What does the handoff include?
Source code, setup notes, architecture context, and recommendations for next iterations and monitoring.
Can we start with a small pilot?
Yes. A single workflow pilot is the fastest way to validate outcomes before expanding scope.
Do you include monitoring and evals?
Yes. We include monitoring hooks and an evaluation baseline so you can iterate safely after launch.
Want an AI pilot for your workflow?
Start with a fixed-scope gig or request a tailored implementation plan for your systems.