Edge AI Deployment
FISTA Solutions deploys AI where the data is created: models optimized for the target hardware, inference tuned within thermal and memory budgets, fleet-wide model updates with rollback, operation that continues without connectivity, and telemetry sized for the link you actually have.
- 150+
- projects delivered
- 50+
- companies served
- 99.9%
- verified uptime
- 47%
- efficiency gains
- 12+
- countries reached
What we build
What does edge AI deployment include?
Edge deployments cover model optimization for the target hardware, inference runtime integration, thermal and power budgeting, fleet model update and rollback, offline operation design, and telemetry that fits constrained links.
- 01
Model optimization
Quantization, pruning, and distillation with quality measured at each step rather than assumed.
Models - 02
Runtime integration
Hardware-appropriate inference runtimes and accelerator use, integrated into the device application.
Runtime - 03
Thermal and power budgets
Sustained-load behavior measured on real devices, with duty cycling where thermals require it.
Constraints - 04
Fleet updates
Staged model rollout across the fleet with health checks and tested rollback.
Lifecycle - 05
Offline operation
Full local operation with queued telemetry and reconciliation when connectivity returns.
Resilience
Requirements
Which requirements shape edge AI deployment?
Edge inference is constrained by hardware and connectivity in ways cloud deployment is not. Requirements cover model size and quality trade-offs, sustained thermal behavior, safe fleet updates, offline correctness, and telemetry that does not saturate the link.
| Requirement | Why it matters | How FISTA implements it |
|---|---|---|
| Size and quality trade-off | Smaller models lose capability unevenly. | Optimization steps evaluated on your golden set so the quality cost of each reduction is known. |
| Thermal behavior | Sustained inference throttles devices. | Sustained-load testing on real hardware with duty cycling and quality tiers where required. |
| Fleet update safety | A bad model can disable a fleet. | Staged rollout with health checks, automatic halt on failure signals, and tested rollback. |
| Offline correctness | Devices must work disconnected. | Full local operation with queued results and reconciliation logic when connectivity returns. |
| Telemetry budget | Bandwidth is expensive or scarce. | Telemetry sampled and aggregated at the edge, sized to the link rather than to what would be convenient. |
Where AI fits
How should you sequence edge AI deployment?
Edge work is sequenced around hardware truth: confirm the target's real capability, optimize with measured quality loss, prove sustained thermal behavior, then build fleet update and offline behavior before scaling.
- 01
1. Confirm hardware capability
What the target device can actually sustain, measured rather than taken from a datasheet.
- 02
2. Optimize with measurement
Each optimization step scored on the golden set so the quality cost is known.
- 03
3. Test sustained load
Thermal and power behavior over realistic duty cycles on real hardware.
- 04
4. Build fleet updates
Staged rollout with health checks and tested rollback before wide deployment.
- 05
5. Design offline behavior
Local operation and reconciliation proven before devices leave connectivity.
Cost and timeline
What does edge AI deployment cost, and how long does it take?
Cost is driven by optimization effort and hardware diversity; timeline by device testing and fleet rollout. FISTA does not quote blind: the scoping call returns a feasibility assessment and a phased estimate.
Hardware diversity multiplies effort. Each device class needs its own optimization, thermal validation, and testing, so the supported matrix is agreed explicitly before the estimate is fixed.
Feasibility comes first. Some models simply will not run usefully on some hardware, and FISTA says so before optimization effort is spent rather than after.
Send the scope you have, even if it is a paragraph. You get a written brief, an architecture sketch, and a phased estimate before any commitment.
Get a scoped quoteDelivery
How does FISTA deliver an AI deployment?
FISTA deploys AI in four phases: an assessment that inventories workloads, data boundaries, and constraints and produces a target architecture; a platform build with networking, identity, secrets, and observability as code; a migration with evaluation gates and shadow traffic; and a production cutover with dashboards, budgets, runbooks, and rollback.
- 1
Assess and target
Workload inventory, data classification, latency and volume profile, compliance constraints, and a target architecture with cost model.
OutputTarget architecture, cost model
- 2
Build the platform
Networking, identity, key management, model endpoints, gateway, tracing, and evaluation pipeline delivered as infrastructure-as-code.
OutputPlatform as code, control matrix
- 3
Migrate with gates
Move applications behind the gateway, run evaluation and shadow traffic, and tune routing, caching, and capacity.
OutputEval reports, shadow results
- 4
Cut over and operate
Graduated production rollout, dashboards for quality, latency, and cost, runbooks, on-call, and a change process with rollback.
OutputProduction platform with SLOs
Why FISTA
Why choose FISTA Solutions for edge AI deployment?
FISTA optimizes edge models with measured quality loss, validates thermal behavior on real hardware, and builds fleet updates that cannot brick a deployment. Work is contracted through a US entity with full IP assignment.
Edge AI specifics
- Every optimization step is scored on your golden set, so quality loss is known rather than hoped to be negligible.
- Thermal and power behavior is measured on real devices under sustained load, not inferred from short benchmarks.
- Fleet model rollout is staged with health checks and tested rollback before any wide deployment.
- Telemetry is aggregated at the edge and sized to the real link rather than to convenience.
How FISTA engineers
- Spec-Driven Development: every deliverable starts as a written specification with acceptance criteria, so scope is testable before it is built.
- AI-native delivery: engineers direct coding agents under review gates and evaluation harnesses, compressing build time without loosening verification.
- Official Anthropic partner, with production experience across Claude, OpenAI, Google, and open-weight models, chosen per workload rather than by default.
- One accountable delivery lead, weekly demos on your environment, and code in your repositories from week one.
What you get as a client
- 150+ projects delivered for 50+ companies across 12+ countries since 2017, with 99.9% verified uptime on systems we operate.
- A US entity (FISTA Solutions Inc., Wilmington, Delaware) for contracting, invoicing, and IP assignment, with an engineering center in Faisalabad, Pakistan for cost-efficient senior capacity.
- US business-hours overlap for standups and reviews; written decision logs so nothing depends on a meeting you missed.
- Flexible engagement: fixed-scope build, embedded forward deployed engineers, or a dedicated team that you can scale month to month.
Clear answers
What platform teams ask before deploying AI.
Straightforward guidance for evaluating scope, fit, and the next step.
01Can large models run on edge devices?
Smaller and optimized models can; large ones generally cannot run usefully within edge memory and thermal budgets. FISTA assesses feasibility against your specific hardware before optimization effort is spent.
02How much quality is lost by optimization?
It varies by model, task, and technique, which is why every optimization step is scored on your golden set. The trade is then a decision rather than an assumption.
03How do we update models across a fleet?
Staged rollout with health checks, automatic halt on failure signals, and tested rollback, because a bad model pushed fleet-wide can disable a deployment.
04Do devices need connectivity?
No. Edge deployments are designed for full local operation with queued telemetry and reconciliation when connectivity returns, which is usually the point of deploying at the edge.
05How long does an edge deployment take?
Optimization and validation typically take weeks to a couple of months per device class, with hardware availability and fleet rollout as the usual gating items.
Scoped in writing before you commit
Run inference where the data is made.
Bring your device targets and use case. The scoping call returns a feasibility assessment, an optimization plan, and an estimate.