Algosoft designs and deploys generative AI systems that automate content, reasoning, and knowledge work at scale. From retrieval-augmented applications and AI agents to document intelligence and fine-tuned models, we help organizations move generative AI from demo to dependable production system.
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Most generative AI initiatives don't fail because the model isn't capable. They fail because the system is ungrounded, ungoverned, and unevaluated — impressive in a controlled demo, unreliable the moment it meets real users and real data.
The failure patterns are consistent across industries — and they usually surface only after a pilot has been promoted to production without the engineering that production requires.
The model generates fluent, confident output that is factually wrong because it answers from training memory rather than your verified content.
Without retrieval over your own data, the system can't cite sources or stay current, so its answers can't be trusted for real decisions.
Sensitive information flows into prompts and third-party models without redaction or controls, creating compliance and confidentiality exposure.
Quality is judged by a few impressive demo runs rather than systematic evaluation, so failures reach production undetected.
Without guardrails, generative systems can be manipulated into unsafe, off-policy, or brand-damaging outputs.
Ungoverned token usage, oversized context, and inefficient architecture make generative features far more expensive than expected at scale.
A promising prototype never ships because it lacks the grounding, guardrails, evaluation, and integration that production demands.
Every organization has different knowledge, different workflows to augment, and different risk tolerance. We design generative AI around your content, processes, and governance requirements — whether you are automating knowledge work, building an internal copilot, or embedding generative features into your product.
Retrieval-augmented applications that answer from your verified content with citations, keeping generative output grounded, current, and trustworthy.
Agentic systems that plan, call tools, and complete multi-step tasks — governed with permissions, guardrails, and audit logging.
Systems that summarize, extract, classify, and answer questions across contracts, reports, and large document sets.
Governed generation of marketing, product, and operational content grounded in your brand voice, facts, and approval workflows
Domain adaptation and fine-tuning where retrieval alone is insufficient, tuning models to your terminology, tone, and tasks.
Generative capabilities built directly into your product or platform through secure, scalable, well-governed APIs.
Evaluation harnesses, safety guardrails, and monitoring that make generative systems measurable, safe, and improvable over time.
Two common approaches adapt a model to your business: grounding it in your data through retrieval, or adapting the model itself through fine-tuning. The right choice depends on how often your knowledge changes, your need for citations, and your data and budget. Both improve relevance, but they differ in freshness, control, and cost.
| Factor | RAG (Retrieval-Augmented) | Fine-Tuning |
|---|---|---|
| Knowledge Freshness | Always current — update the content | Frozen at training time until retrained |
| Best For | Changing knowledge, factual answers | Fixed style, tone, or specialized tasks |
| Source Citation | Can cite exact sources | Cannot cite where output came from |
| Data Requirements | Works with your existing documents | Needs curated training examples |
| Hallucination Control | Grounding reduces fabrication | Reduces some errors, not grounding |
| Upfront Cost | Lower — no training run | Higher — data prep and training |
| Maintenance | Update content, not the model | Retrain to update knowledge |
← Swipe horizontally to compare approaches →
If your knowledge changes frequently and answers must be current and cite their sources, retrieval-augmented generation is usually the stronger foundation — you update the content and the system follows.
If you need a consistent style, specialized terminology, or a narrow task where retrieval alone falls short, fine-tuning adapts the model itself — provided you can curate quality training data.
In practice, many production systems combine both: retrieval for current, citable facts and fine-tuning for consistent tone and specialized behavior, chosen by the realities of your knowledge and obligations.
The best generative system isn't built around whichever model launched this month — it's built around your accuracy needs, data residency, cost targets, and governance obligations. We work across hosted and open-weight ecosystems, selecting what fits your environment.
Deploy hosted or open-weight models based on accuracy, cost, and data-residency needs.
Which technologies fit your data, analytics, or AI roadmap? We'll help you select the right stack.
Consult NowGround generation in your content and orchestrate multi-step reasoning and tool use.
Which technologies fit your data, analytics, or AI roadmap? We'll help you select the right stack.
Consult NowPower semantic retrieval over your knowledge base with fast, relevant search.
Which technologies fit your data, analytics, or AI roadmap? We'll help you select the right stack.
Consult NowBuild agents that plan, call tools, and complete tasks under governed permissions.
Which technologies fit your data, analytics, or AI roadmap? We'll help you select the right stack.
Consult NowAdapt models to your domain, tone, and tasks where retrieval alone is insufficient.
Which technologies fit your data, analytics, or AI roadmap? We'll help you select the right stack.
Consult NowMeasure quality and enforce safety, grounding, and policy before and after launch.
Which technologies fit your data, analytics, or AI roadmap? We'll help you select the right stack.
Consult NowExtract, summarize, and answer over large, unstructured document sets.
Which technologies fit your data, analytics, or AI roadmap? We'll help you select the right stack.
Consult NowDeploy on your cloud or private infrastructure where data residency requires it.
Which technologies fit your data, analytics, or AI roadmap? We'll help you select the right stack.
Consult NowNo two generative systems are the same. The effort depends on how your content is organized, the complexity of the tasks, the governance your sector requires, and the evaluation needed to make output trustworthy.
Clean, well-structured content grounds quickly. Fragmented, outdated, or contradictory documentation often requires cleanup that exceeds the application build itself.
Single-step generation is predictable. Multi-step agents, tool use, and complex reasoning require deeper design, testing, and guardrails.
Reliable retrieval over large or messy content requires chunking, indexing, and relevance tuning that materially affect quality and effort.
Regulated and customer-facing systems require PII redaction, guardrails, content filtering, and audit logging that expand scope.
Building evaluation harnesses and curated test sets to measure accuracy, grounding, and safety is essential and adds a dedicated phase.
Where fine-tuning is required, data curation, training, and validation add significant effort beyond a retrieval-only approach.
Embedding generative features into your product, systems, and workflows adds integration, security, and testing effort.
Token usage, context size, and inference cost must be engineered and monitored to keep generative features economical at scale.
Every generative AI initiative has different delivery needs. Whether you're building a new generative application, moving a pilot to production, or improving a system already live, we offer engagement models tailored to your objectives, timeline, and internal capabilities.
| Model | Works Best When | Trade-Off |
|---|---|---|
| Dedicated Team | You're building generative capability across multiple use cases that requires continuous iteration and governance. | Requires active involvement and ongoing alignment from your internal stakeholders. |
| Project-Based | You have a clearly defined generative application, content set, and integration scope. | Discovery findings may occasionally require scope refinement as implementation progresses. |
| Monthly Retainer | You need ongoing evaluation, guardrail tuning, content updates, and cost optimization. | Less suitable for one-time implementations or short-term projects. |
| Hourly | Best for RAG audits, prompt and evaluation reviews, troubleshooting, and targeted guidance. | Not designed for large-scale generative implementations. |
← Swipe horizontally to compare options →
We follow a structured delivery framework that helps organizations build grounded, safe, and maintainable generative systems — from knowledge assessment and grounding design through evaluation, deployment, and continuous improvement.
We begin by understanding the task to augment, your content sources, and the accuracy and safety requirements to define a clear roadmap.
Our team designs the retrieval strategy, prompting approach, agent logic, and guardrails required for grounded, safe output.
We connect the system to your content and business systems while ensuring accuracy, security, and maintainability.
We build the generative application, orchestration, guardrails, and integrations following engineering best practices.
Every system is evaluated for accuracy, grounding, safety, and cost against curated test sets before production deployment.
After launch, we monitor output, tune guardrails, and continuously improve the system as content and usage evolve.
Delivering tailored, scalable software solutions to enhance efficiency and drive digital transformation across industries.
Building a reliable generative system requires more than access to a powerful model. It demands grounding in your data, strong governance, systematic evaluation, and a focus on the safety and cost control that production demands.
Our systems answer from your verified content using retrieval-augmented generation, cite their sources, and decline when they lack grounding — reducing hallucination where it matters most.
We build evaluation harnesses and curated test sets so quality, grounding, and safety are measured systematically before launch, not judged by a few demo runs.
PII redaction, guardrails, content filtering, and audit logging are built in, governed by our ISO 27001:2023 and ISO 42001:2023 certifications.
We engineer retrieval, context, and model choice to keep generative features economical and monitor token usage so costs stay predictable at scale.
Our delivery practices are backed by ISO 9001:2015, ISO 27001:2023, ISO 42001:2023, and CMMI Level 3 Appraised standards, helping ensure quality, security, and operational maturity.
Source code, prompt configurations, retrieval setup, and fine-tuning artefacts transfer to you. No proprietary runtime and no licence fee on the intelligence layer.
Whether you are dealing with a stalled pilot, ungrounded output, governance concerns, or a workflow you want to augment with generative AI, our engineers can help you design the right grounding strategy, guardrails, evaluation approach, and integration plan before development begins.
We partner with businesses across Africa, the Middle East, Southeast Asia, Europe, Australia, and North America, delivering custom software solutions that align with local business needs while supporting global operations. Our distributed development approach enables seamless collaboration across time zones, transparent communication, and consistent project delivery—whether you're building a new digital product, modernizing enterprise systems, or extending your in-house engineering team.
Find answers to common questions about our services, process, timelines, and collaboration model.
A focused RAG application on well-structured content typically reaches production in eight to twelve weeks. Multi-agent systems, fine-tuning, and deep integrations run four to six months. Knowledge base condition is the most common cause of variance.
Cost depends on task complexity, grounding and retrieval design, governance depth, and evaluation needs. Rather than quoting a range that will not match your situation, we scope against your actual requirements during discovery and provide a fixed estimate before any build commitment.
Through retrieval-augmented generation, so the system answers from your verified content and cites sources rather than fabricating from memory, reinforced by guardrails and systematic evaluation against a curated test set before launch.
For knowledge that changes and answers that must cite sources, RAG is usually the stronger foundation. For consistent style or specialized tasks where retrieval falls short, fine-tuning helps. Many production systems combine both; we recommend the right mix during discovery.
We apply PII redaction before model processing, enforce access controls and audit logging, and—where data residency or regulation requires it—deploy open-weight models on your own infrastructure so data never leaves your environment.
Get More InformationYes. Source code, prompt configurations, retrieval setup, and any fine-tuning artefacts transfer to you. There is no proprietary runtime and no licence fee on the intelligence layer. Third-party model API costs, where applicable, are billed directly to your account.
We engineer retrieval, context size, and model selection for efficiency, and monitor token usage in production so costs stay predictable and features remain economical as usage scales.
We build an evaluation harness and a curated test set that measure accuracy, grounding, and safety systematically, so the system is validated against real requirements rather than judged by a handful of impressive demo runs.
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If you have a project in mind, let’s talk.
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