BitDocs interlinked chain markBitDocs

Services

Concrete deliverables, indicative timelines and the tools we actually use. Timelines assume reasonable access to your data and stakeholders; fixed-price scope is agreed after a short discovery call.

AI Journey

Pick the two use cases worth building, and prove them.

We run interviews and data checks across your processes, score candidate use cases on value and feasibility, then build one working prototype on your own data so the decision is based on evidence rather than a slide.

What you get

  • Use-case inventory with value/effort scoring and a go/no-go recommendation
  • Data readiness note: sources, access, quality gaps, blockers
  • One prototype on your own data (RAG search, extraction or assistant)
  • Cost model per 1k requests plus target accuracy/latency numbers
Indicative timeline
3–6 weeks · 1 architect + 1 engineer
Typical stack
OpenAI / Anthropic / Gemini · pgvector or Qdrant · Python, TypeScript · LangGraph
AI workstation showing embedding clusters and knowledge graphs used in a BitDocs AI journey engagement

Solution Design

A build-ready specification, not a wish list.

We map the current system, define target architecture and data flows, and specify each interface down to payloads and failure behaviour, so any competent team could implement it — including yours, if you prefer.

What you get

  • Architecture diagrams (context, container, sequence) with decision records
  • Data model, integration map and API contracts (OpenAPI / JSON schemas)
  • Clickable UI prototype for the main flows
  • Backlog with estimates and a phased delivery plan
Indicative timeline
2–5 weeks · fixed price on agreed scope
Typical stack
Figma · OpenAPI · PostgreSQL · Mermaid / C4
Architect drawing a system flow on a glass board surrounded by architecture diagrams

Implementation & Engineering

Two-week increments, each deployed to a real environment.

A senior pair or trio builds the system in short increments. Every increment ends on staging with tests, migrations and a changelog, so progress is verifiable instead of reported.

What you get

  • Production and staging environments with CI/CD and rollback
  • Application code in your repository, with tests and migrations
  • Observability: structured logs, metrics, alerting, error tracking
  • Runbook and hand-over session for your team
Indicative timeline
6–16 weeks typical · demo every 2 weeks
Typical stack
TypeScript, React, Node · Python · Postgres / Supabase · Docker, GitHub Actions · Cloudflare / AWS
Data center rack with glowing network cabling representing cloud-native implementation and DevOps

Custom Agent Design, Implementation & Orchestration

Agents with versioned prompts, tools and an eval suite.

We build agents on LayerOS: prompts, modules and libraries are versioned assets, orchestration is explicit, and every change is measured against a regression set before it reaches production.

What you get

  • Agent catalogue: role, allowed tools, escalation and approval rules
  • Versioned prompt and module library with review history
  • Orchestration workflow (routing, hand-offs, retries, human approval gates)
  • Eval suite with a labelled test set and a per-release scorecard
  • Trace-level logging of every run, input and tool call
Indicative timeline
4–10 weeks to first production workflow
Typical stack
LayerOS · LangGraph · Function calling / MCP · pgvector · Langfuse-style tracing
Stacked translucent glass layers illustrating the LayerOS framework for custom AI agents

Scale & Support

A named engineer, agreed response times, monthly numbers.

After go-live we keep the system healthy: patching, cost and latency tuning, model upgrades, and a monthly review of what actually happened in production.

What you get

  • SLA with response targets (business hours or extended cover)
  • Dashboards for latency, cost, error and accuracy drift
  • Monthly report: incidents, spend, model and prompt changes
  • Quarterly security and dependency review
Indicative timeline
Monthly retainer · from 0.2 FTE
Typical stack
Grafana / Sentry-class tooling · Uptime monitoring · Dependency scanning
Layered glass architecture visual representing scaling and operating a running AI product

System Integration

One connector, one contract, idempotent and monitored.

We connect ERP, CRM, storage and SaaS tools with explicit contracts, retry and replay behaviour, so failures are visible and recoverable instead of silent.

What you get

  • Connectors with schema validation, retries, idempotency keys and replay
  • Field-level mapping document per integrated system
  • Reconciliation job and exception queue for failed records
  • Load test results and documented throughput limits
Indicative timeline
2–8 weeks per integration
Typical stack
REST / GraphQL / SFTP · Webhooks & queues · SAP, Dynamics, Salesforce, HubSpot · dbt-style pipelines
Burj Khalifa and the Dubai skyline at night with data overlays, representing system integration work

Our framework

LayerOS

LayerOS is how we keep custom agents maintainable: four layers of reusable assets, each versioned, reviewed, and traceable back to its source.

01

Prompt Layer

Every agent instruction is a first-class, versioned asset with changelogs, provenance, and review — not a string buried in code.

02

Module Layer

Capabilities are packaged as composable modules: tools, knowledge access, guardrails, and evaluation hooks that agents share.

03

Library Layer

Modules and prompts are bundled into role libraries — engineering, product, growth, operations — ready to deploy per client.

04

Orchestration Layer

Agents are coordinated into workflows with routing, hand-offs, approvals, and observability across every run.

Orchestration in practice

Example workflow: invoice-to-ERP with five agents

A real LayerOS orchestration pattern. Each agent has a narrow contract — explicit input, explicit output — so every run is testable, observable, and safe to hand back to a human.

  1. Intake AgentL1 · Prompt LayerT+0s

    Input

    Incoming supplier invoice (PDF) dropped in the finance inbox

    Output

    Normalised document record + routing decision (invoice / contract / other)

    BranchNot an invoice → parked for review, workflow stops here

  2. Extraction AgentL2 · Module LayerT+8s

    Input

    Document record + OCR and schema-validation modules

    Output

    Structured JSON: supplier, VAT ID, line items, totals, currency, due date

    BranchSchema validation fails → retry with fallback parser, then escalate

  3. Compliance AgentL3 · Library LayerT+15s

    Input

    Extracted JSON + finance role library (VAT rules, PO matching, duplicate checks)

    Output

    Risk score, matched purchase order, list of exceptions with evidence links

    BranchDuplicate detected → flagged, no posting

  4. Supervisor AgentL4 · Orchestration LayerT+22s

    Input

    Risk score + exception list + approval policy thresholds

    Output

    Auto-approve under AED 10k, or human approval task with a one-screen summary

    BranchAbove threshold → human in the loop before anything is posted

  5. Sync AgentL4 · Orchestration LayerT+30s

    Input

    Approved invoice payload

    Output

    Posted to ERP, audit trail written, Slack notification to the finance owner

Same pattern, different libraries: KYC onboarding, tender responses, support triage, or reporting.

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